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28 results on '"Emmanuel Mazer"'

1. Cognitive Computation

Author

Raphaël Laurent, Mohamad Othman Abdallah, Jorge Lobo, Emmanuel Mazer, Marvin Faix, and Ronan Le Hy

Subjects
Computer science, business.industry, Computation, Cognition, 02 engineering and technology, Machine learning, computer.software_genre, Bayesian inference, 020202 computer hardware & architecture, Bayesian statistics, Relevance vector machine, Frequentist inference, 0202 electrical engineering, electronic engineering, information engineering, Fiducial inference, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer
Abstract

Probabilistic programming allows artificial systems to better operate with uncertainty, and stochastic arithmetic provides a way to carry out approximate computations with few resources. As such, both are plausible models for natural cognition. The authors' work on the automatic design of probabilistic machines computing soft inferences, with an arithmetic based on stochastic bitstreams, allowed to develop the following compilation toolchain: given a high-level description of some general problem, formalized as a Bayesian Program, the toolchain automatically builds a low-level description of an electronic circuit computing the corresponding probabilistic inference. This circuit can then be implemented and tested on reconfigurable logic. This paper describes two circuits as validating examples. The first one implements a Bayesian filter solving the problem of Pseudo Noise sequence acquisition in telecommunications. The second one implements decision making in a sensorimotor system: it allows a simple robot to avoid obstacles using Bayesian sensor fusion.

Published
2017
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2. An n-Bit Adder Realized via Coherent Optical Parallel Computing

Author

Maurizio Coden, Carlo Nazareno Dibenedetto, Hugo Gattuso, Marinella Striccoli, Noam Gross, Ariela Donval, Barbara Fresch, Françoise Remacle, Elisabetta Collini, Yossi Paltiel, Bogdan Reznychenko, and Emmanuel Mazer

Subjects
Adder, Computer science, Computation, 02 engineering and technology, Parallel computing, 010402 general chemistry, 021001 nanoscience & nanotechnology, 01 natural sciences, 0104 chemical sciences, Nonlinear system, Superposition principle, Quantum state, SIMD, 0210 nano-technology, Coherent spectroscopy, Quantum
Abstract

The quantum properties of nanosystems present a new opportunity to enhance the power of classical computers, both for the parallelism of the computation and the speed of the optical operations. In this paper we present the COPAC project aiming at development of a ground-breaking nonlinear coherent spectroscopy combining optical addressing and spatially macroscopically resolved optical readout. The discrete structure of transitions between quantum levels provides a basis for implementation of logic functions even at room temperature. Exploiting the superposition of quantum states gives rise to the possibility of parallel computation by encoding different input values into transition frequencies. As an example of parallel single instruction multiple data calculation by a device developed during the COPAC project, we present a n-bit adder, showing that due to the properties of the system, the delay of this fundamental circuit can be reduced.

Published
2019
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3. Bayesian time-domain multiple sound source localization for a stochastic machine

Author

Emmanuel Mazer, Laurent Girin, Marvin Faix, Raphael Frisch, Jacques Droulez, Techniques of Informatics and Microelectronics for integrated systems Architecture (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Interaction située avec les objets et environnements intelligents (PERVASIVE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), CRISSP (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-GIPSA Pôle Parole et Cognition (GIPSA-PPC), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Techniques de l'Informatique et de la Microélectronique pour l'Architecture des systèmes intégrés (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])

Subjects
[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], Signal processing, Computer science, Bayesian probability, 020206 networking & telecommunications, Statistical model, specific hardware, 02 engineering and technology, Acoustic source localization, Bayesian stochastic machine, time-domain processing, Bayesian inference, Robustness (computer science), [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Multiple sound source localization, Time domain, Algorithm, ComputingMilieux_MISCELLANEOUS
Abstract

We propose a time-domain multiple sound source localization (SSL) method based on Bayesian inference. This method is specifically designed to run on the stochastic machines (SM) that we are currently developing to perform efficient low-level sensor signal processing with ultra-low power consumption. The proposed SSL method is divided into two main parts. First, a probabilistic model is run on 50 very short time frames (3. 75ms each) of multichannel recorded signals. Second, the results obtained on the different frames are fused to obtain a final localization map. Using the system in a supervised way allows to extract estimated source locations by selecting as many maxima as there are sources in the room. We explain how this method is implemented on a SM. Experiments are presented to illustrate the performance and robustness of the resulting system.

Published
2019
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4. A cognitive stochastic machine based on Bayesian inference: a behavioral analysis

Author

Marvin Faix, Laurent Fesquet, Augustin Lux, Raphael Frisch, Emmanuel Mazer, Techniques de l'Informatique et de la Microélectronique pour l'Architecture des systèmes intégrés (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Interaction située avec les objets et environnements intelligents (PERVASIVE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), ANR-11-LABX-0025,PERSYVAL-lab,Systemes et Algorithmes Pervasifs au confluent des mondes physique et numérique(2011), Techniques of Informatics and Microelectronics for integrated systems Architecture (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), and ANR-11-LABX-0025-01,PERSYVAL-lab,Systèmes et Algorithmes Pervasifs au confluent des mondes physique et numérique(2011)

Subjects
Optimal design, [INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], Stochastic computing, Computer science, Stochastic process, Computation, Bayesian probability, Probabilistic logic, 02 engineering and technology, Sensor fusion, Bayesian inference, 030507 speech-language pathology & audiology, 03 medical and health sciences, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, 0305 other medical science, Algorithm
Abstract

International audience; Bayesian models and stochastic computing form a promising paradigm for non-conventional, bio-inspired computation architectures. In particular, they are able to handle uncertainty and promise low power consumption. In this paper we study the application of such an architecture, the Sliced Bayesian Machine (SlicedBM) to a real-world problem, Sound Source Localization (SSL) for robots. We present an analysis of the quality of results and of computing time according to several parameters: sensor precision, result threshold, internal word length. Furthermore, we show that sensor data precision does not heavily influence the computation. On the opposite, the precision of the probability values plays an important role on result quality. This parameter also determines the circuit size. We also show that the higher the re-sampling threshold (RT), the better the distribution computed by the machine. Our results make it possible to choose optimal design parameters for a circuit along several trade-offs, and according to a given sensor fusion application.

Published
2018

5. A Bayesian stochastic machine for sound source localization

Author

Laurent Girin, Raphaël Laurent, Pierre Bessière, Laurent Fesquet, Raphael Frisch, Emmanuel Mazer, Augustin Lux, Jacques Droulez, Marvin Faix, Techniques de l'Informatique et de la Microélectronique pour l'Architecture des systèmes intégrés (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Interaction située avec les objets et environnements intelligents (PERVASIVE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Probayes [Montbonnot], GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Interpretation and Modelling of Images and Videos (PERCEPTION ), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), ANR-11-LABX-0025,PERSYVAL-lab,Systemes et Algorithmes Pervasifs au confluent des mondes physique et numérique(2011), Techniques of Informatics and Microelectronics for integrated systems Architecture (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), CRISSP (GIPSA-CRISSP), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-GIPSA Pôle Parole et Cognition (GIPSA-PPC), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Interpretation and Modelling of Images and Videos (PERCEPTION), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut National Polytechnique de Grenoble (INPG), and ANR-11-LABX-0025-01,PERSYVAL-lab,Systèmes et Algorithmes Pervasifs au confluent des mondes physique et numérique(2011)

Subjects
[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], Stochastic computing, Computer science, Stochastic process, Posterior probability, Bayesian probability, 02 engineering and technology, Acoustic source localization, Bayesian inference, 030507 speech-language pathology & audiology, 03 medical and health sciences, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 0202 electrical engineering, electronic engineering, information engineering, Probability distribution, 020201 artificial intelligence & image processing, 0305 other medical science, Likelihood function, Algorithm
Abstract

International audience; Compared to conventional processors, stochastic computing architectures have strong potential to speed up computation time and to reduce power consumption. We present such an architecture, called Bayesian Machine (BM), dedicated to solving Bayesian inference problems. Given a set of noisy signals provided by low-level sensors, a BM estimates the posterior probability distribution of an unknown target information. In the present study, a BM is used to solve a sound source localization (SSL) problem: the BM computes the probability distribution of the position of a sound source given acoustic signals captured by a set of microphones. Assuming free field wave propagation (no reverberations), we express the SSL problem as the maximization of a likelihood function fed with audio features provided by the time-frequency (TF) analysis of the captured audio waves. The proposed BM uses bitwise parallel sampling to fuse the resulting multi-channel information. As the number of channels to fuse is large, the standard BM architecture encounters the so-called " time dilution problem " (long delays are necessary to obtain valid samples). We tackle this problem by using max-normalization of the distributions combined with a periodic re-sampling of the bit streams after processing a reasonably small subset of evidences. Finally, we compare the localization performance of the proposed machine with the results obtained using a standard version of the machine. The re-sampling leads to an impressive acceleration factor of 10³ in the computation.

Published
2017

6. Bayesian Sensor Fusion with Fast and Low Power Stochastic Circuits

Author

Raphaël Laurent, Jorge Lobo, Jacques Droulez, Pierre Bessière, M. Awais Aslam, Emmanuel Mazer, Alexandre Coninx, Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), AMAC, Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Probayes [Montbonnot], and University of Coimbra

Subjects
[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], Theoretical computer science, Computer science, 020208 electrical & electronic engineering, Bayesian probability, Probabilistic logic, [SCCO.COMP]Cognitive science/Computer science, 02 engineering and technology, Energy consumption, Sensor fusion, Bayesian inference, 020202 computer hardware & architecture, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Computer Science::Hardware Architecture, Computer engineering, Probabilistic logic network, Logic gate, 0202 electrical engineering, electronic engineering, information engineering, Applications of artificial intelligence
Abstract

International audience; —As the physical limits of Moore's law are being reached, a research effort is launched to achieve further performance improvements by exploring computation paradigms departing from standard approaches. The BAMBI project (Bottom-up Approaches to Machines dedicated to Bayesian Inference) aims at developing hardware dedicated to probabilistic computation , which extends logic computation realised by boolean gates in current computer chips. Such probabilistic computing devices would allow to solve faster and at a lower energy cost a wide range of Artificial Intelligence applications, especially when decisions need to be taken from incomplete data in an uncertain environment. This paper describes an architecture where very simple operators compute on a time coding of probability values as stochastic signals. Simulation tests and a reconfigurable logic hardware implementation demonstrated the feasibility and performances of the proposed inference machine. Hardware results show this architecture can quickly solve Bayesian sensor fusion problems and is very efficient in terms of energy consumption.

Published
2016

7. Evidences of Stochastic Bayesian Machines Robustness Against SEUs and SETs

Author

Raphaël Laurent, Alexandre Siqueira Guedes Coelho, Miguel Solinas, S. Karaoui, Raoul Velazco, Juan A. Fraire, Emmanuel Mazer, Nacer-Eddine Zergainoh, Techniques of Informatics and Microelectronics for integrated systems Architecture (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Probayes [Montbonnot], Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Université des sciences et de la Technologie d'Oran Mohamed Boudiaf [Oran] (USTO MB), Techniques de l'Informatique et de la Microélectronique pour l'Architecture des systèmes intégrés (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and Laboratoire d'Informatique de Grenoble (LIG )

Subjects
010308 nuclear & particles physics, Stochastic process, Computer science, business.industry, Computation, Bayesian probability, Probabilistic logic, 02 engineering and technology, Machine learning, computer.software_genre, 01 natural sciences, 020202 computer hardware & architecture, PACS 8542, Robustness (computer science), 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Probability distribution, Artificial intelligence, [SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics, business, computer
Abstract

International audience; This work revisits the stochastic computationparadigm as a way to implement architectures dedicated toBayesian computation. It is assumed that Stochastic BayesianMachines (SMBs) are intrinsically tolerant to the effects ofradiation. However, practical assessment is mandatory beforeconsidering SBMs in hazardous environments. Results of faultinjectioncampaigns performed at the RTL level provide the firstevidences of SBMs robustness with respect to SEUs and SETs.

Published
2016

8. Autonomous Robot Controller Using Bitwise GIBBS Sampling

Author

Raphaël Laurent, Emmanuel Mazer, Marvin Faix, Dominique Vaufreydaz, Rémi Canillas, PERVASIVE INTERACTION (PERVASIVE INTERACTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Probayes [Montbonnot], Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut d'Informatique et de Mathématiques Appliquées de Grenoble (IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)

Subjects
Computer science, Bayesian programming, 01 natural sciences, Computer Science::Robotics, symbols.namesake, Gibbs sampling, Control theory, 0103 physical sciences, Obstacle avoidance, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Bitwise operation, Stochastic computing, 010308 nuclear & particles physics, business.industry, Control engineering, Autonomous robot, [MATH.MATH-PR]Mathematics [math]/Probability [math.PR], autonomous robots, symbols, stochastic computing, [INFO.INFO-ES]Computer Science [cs]/Embedded Systems, Artificial intelligence, business, Von Neumann architecture
Abstract

International audience; In the present paper we describe a bio-inspired non von Neumann controller for a simple sensorimotor robotic system. This controller uses a bitwise version of the Gibbs sampling algorithm to select commands so the robot can adapt its course of action and avoid perceived obstacles in the environment. The VHDL specification of the circuit implementation of this controller is based on stochastic computation to perform Bayesian inference at a low energy cost. We show that the proposed unconventional architecture allows to successfully carry out the obstacle avoidance task and to address scalability issues observed in previous works.

Published
2016

9. Design of Stochastic Machines Dedicated to Approximate Bayesian inferences

Author

Jacques Droulez, Pierre Bessière, Raphaël Laurent, Marvin Faix, Emmanuel Mazer, PERVASIVE INTERACTION (PERVASIVE INTERACTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Probayes [Montbonnot], Centre National de la Recherche Scientifique (CNRS), Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), AMAC, Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), European Project: 618024,EC:FP7:ICT,FP7-ICT-2013-C,BAMBI(2014), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], Theoretical computer science, Computer science, Bayesian probability, Inference, [SCCO.COMP]Cognitive science/Computer science, 02 engineering and technology, 01 natural sciences, Bayesian Programming, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], symbols.namesake, Joint probability distribution, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Computer Science (miscellaneous), Stochastic computing, 010308 nuclear & particles physics, 020206 networking & telecommunications, Computer Science Applications, Human-Computer Interaction, Bayesian statistics, symbols, stochastic computing, Stochastic optimization, Information Systems, Gibbs sampling, Von Neumann architecture
Abstract

International audience; We present an architecture and a compilation toolchain for stochastic machines dedicated to Bayesian inferences. These machines are not Von Neumann and code information with stochastic bitstreams instead of using floating point representations. They only rely on stochastic arithmetic and on Gibbs sampling to perform approximate inferences. They use banks of binary random generators which capture the prior knowledge on which the inference is built. The output of the machine is devised to continuously sample the joint probability distribution of interest. While the method is explained on a simple example, we show that our machine computes a good approximation of the solution to a problem intractable in exact inference.

Published
2016
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10. Cognitive computation: A Bayesian machine case study

Author

Jorge Lobo, Raphaël Laurent, Mohamad Othman Abdallah, Ronan Le Hy, Emmanuel Mazer, and Marvin Faix

Subjects
Sequence, Theoretical computer science, business.industry, Computer science, Computation, Carry (arithmetic), Bayesian probability, Probabilistic logic, Toolchain, symbols.namesake, symbols, Noise (video), Artificial intelligence, business, Von Neumann architecture
Abstract

The work presented in this paper is part of the BAMBI project, which aims at better understanding natural cognition by designing non Von Neumann machines with biologicaly plausible hardware. Probabilistic programming allows artificial systems to better operate with uncertainty, and stochastic arithmetic provides a way to carry out approximate computations with few resources. As such, both are plausible models for natural cognition. Our work on the automatic design of probabilistic machines computing soft inferences with an arithmetic based on stochastic bitstreams allowed us to develop the following compilation toolchain: given a high level description of some general problem (typically to infer some knowledge from a model given some observations), formalized as a Bayesian Program, our toolchain automatically builds a low level description of an electronic circuit computing the corresponding probabilistic inference. This circuit can then be implemented and tested on reconfigurable logic.We designed as a validating example a circuit description of a Bayesian filter solving the problem of Pseudo Noise sequence acquisition in telecommunications.

Published
2015
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11. The design and implementation of a Bayesian CAD modeler for robotic applications

Author

Pierre Bessière, Kamel Mekhnacha, Emmanuel Mazer, Laboratoire Leibniz (Leibniz - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Automatic Programming and Decisional Systems in Robotics (SHARP), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, and Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
0209 industrial biotechnology, Mathematical optimization, geometric constraints, Computer science, Generalization, Monte Carlo method, Bayesian probability, Bayesian reasoning, [SCCO.COMP]Cognitive science/Computer science, CAD, 02 engineering and technology, Bayesian inference, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 020901 industrial engineering & automation, 0202 electrical engineering, electronic engineering, information engineering, business.industry, Monte Carlo methods, Robotics, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], Computer Science Applications, Human-Computer Interaction, Constraint (information theory), Hardware and Architecture, Control and Systems Engineering, Probability distribution, 020201 artificial intelligence & image processing, Artificial intelligence, business, Software
Abstract

International audience; We present a Bayesian CAD modeler for robotic applications. We address the problem of taking into account the propagation of geometric uncertainties when solving inverse geometric problems. The proposed method may be seen as a generalization of constraint-based approaches in which we explicitly model geometric uncertainties. Using our methodology, a geometric constraint is expressed as a probability distribution on the system parameters and the sensor measurements, instead of a simple equality or inequality. To solve geometric problems in this framework, we propose an original resolution method able to adapt to problem complexity. Using two examples, we show how to apply our approach by providing simulation results using our modeler.

Published
2001
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12. Bayesian Programming Formalism

Author

Pierre Bessière, Emmanuel Mazer, Kamel Mekhnacha, and Juan Ahuactzin

Subjects
Algebra, Formalism (philosophy), Computer science, Bayesian programming
Published
2013
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13. On the Robustness of Stochastic Bayesian Machines

Author

Emmanuel Mazer, Nacer-Eddine Zergainoh, Raoul Velazco, Miguel Solinas, Juan A. Fraire, Raphaël Laurent, Said Karaoui, Alexandre Siqueira Guedes Coelho, Techniques of Informatics and Microelectronics for integrated systems Architecture (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Probayes [Montbonnot], Centre National de la Recherche Scientifique (CNRS), Techniques de l'Informatique et de la Microélectronique pour l'Architecture des systèmes intégrés (TIMA), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])

Subjects
Nuclear and High Energy Physics, Stochastic computing, 010308 nuclear & particles physics, Computer science, Stochastic process, Distributed computing, Bayesian probability, Probabilistic logic, 02 engineering and technology, Fault injection, Bayesian inference, 01 natural sciences, 020202 computer hardware & architecture, PACS 8542, Nuclear Energy and Engineering, Robustness (computer science), 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, [SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics, Electrical and Electronic Engineering, Register-transfer level
Abstract

This paper revisits the stochastic computing paradigm as a way to implement architectures dedicated to probabilistic inference. In general, it is assumed the operation over stochastic bit streams is robust with respect to radiation transient events effects. Moreover, it can be expected that leveraging the stochastic computing paradigm to implement probabilistic computations such as Bayesian inference implemented in hardware could yield an increased resilience to radiation effects comparatively to deterministic procedures. However, the practical assessment of the robustness against radiation is mandatory before considering stochastic Bayesian machines (SBMs) in hazardous environments. Results of fault injection campaigns at register transfer level provide the first evidences of the intrinsic robustness of SBMs with respect to single event upsets and single event transients.

Published
2017
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14. Corruption risk analysis using semi-supervised naïve Bayes classifiers

Author

Remis Balaniuk, Pierre Bessière, Emmanuel Mazer, Paulo Roberto Cobbe, Mestrado em Gestão do Conhecimento e da Tecnologia da Informação (UCB/MGCTI), Universidade Católica de Brasília=Catholic University of Brasília (UCB), Laboratoire de Physiologie de la Perception et de l'Action (LPPA), Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS), Geometry and Probability for Motion and Action (E-MOTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Universidade Católica de Brasília, and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)

Subjects
Risk analysis, Government, Actuarial science, General Computer Science, Corruption, Computer science, media_common.quotation_subject, General Engineering, Probabilistic logic, [SCCO.COMP]Cognitive science/Computer science, ComputingMilieux_LEGALASPECTSOFCOMPUTING, 02 engineering and technology, Audit, 16. Peace & justice, Computer security, computer.software_genre, Naive Bayes classifier, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Risk assessment, computer, media_common
Abstract

International audience; In this paper, we consider the application of a naïve Bayes model for the evaluation of corruption risk associated with government agencies. This model applies probabilistic classifiers to support a generic risk assessment model, allowing for more efficient and effective use of resources for the detection of corruption in government transactions, and assisting audit agencies in becoming more proactive regarding corruption detection and prevention.

Published
2013
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15. The Ariadne's Clew Algorithm

Author

Juan-Manuel Ahuactzin, Pierre Bessière, Emmanuel Mazer, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), Institut National Polytechnique de Grenoble (INPG), Automatic Programming and Decisional Systems in Robotics (SHARP), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria), Institut d'Informatique et de Mathématiques Appliquées de Grenoble (IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Leibniz (Leibniz - IMAG), Laboratoire de Génie Informatique (LGI - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-IMAG, Laboratoire de Génie Informatique - LAboratoire de SCiences COgnitives (IMAG-LGI / LASCO3), IMAG, Bessiere, Pierre, Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Université Joseph Fourier - Grenoble 1 (UJF)-IMAG-Centre National de la Recherche Scientifique (CNRS), Departamento de Ingeniería En Sistemas Computacionales, Universidad de las Américas, and Laugier, Christian

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], FOS: Computer and information sciences, 0209 industrial biotechnology, Optimization problem, Computer science, Computer Science - Artificial Intelligence, [SCCO.COMP]Cognitive science/Computer science, 02 engineering and technology, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 020901 industrial engineering & automation, Artificial Intelligence, [SCCO.COMP] Cognitive science/Computer science, 0202 electrical engineering, electronic engineering, information engineering, Six degrees of freedom, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Motion planning, Representation (mathematics), ComputingMilieux_MISCELLANEOUS, Degrees of freedom, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Artificial Intelligence (cs.AI), Obstacle, Path (graph theory), Robot, 020201 artificial intelligence & image processing, Algorithm
Abstract

http://www.jair.org/papers/paper468.html; International audience; We present a new approach to path planning, called the Ariadne's clew algorithm. It is designed to find paths in high-dimensional continuous spaces and applies to robots with many degrees of freedom in static, as well as dynamic environments --- ones where obstacles may move. The Ariadne's clew algorithm comprises two sub-algorithms, called SEARCH and EXPLORE, applied in an interleaved manner. EXPLORE builds a representation of the accessible space while SEARCH looks for the target. Both are posed as optimization problems. We describe a real implementation of the algorithm to plan paths for a six degrees of freedom arm in a dynamic environment where another six degrees of freedom arm is used as a moving obstacle. Experimental results show that a path is found in about one second without any pre-processing.

Published
2011
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16. Bayesian Approach to Action Selection and Attention Focusing

Author

Emmanuel Mazer, Pierre Bessière, Carla Koike, Geometry and Probability for Motion and Action (E-MOTION), Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0209 industrial biotechnology, Computer science, business.industry, Bayesian probability, [SCCO.COMP]Cognitive science/Computer science, Mobile robot, 02 engineering and technology, Machine learning, computer.software_genre, Action selection, Motor model, 020901 industrial engineering & automation, Joint probability distribution, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 020201 artificial intelligence & image processing, Artificial intelligence, business, computer
Published
2008
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17. A practical system for planning safe trajectories for manipulator robots

Author

Christian Laugier, Jocelyne Troccaz, Emmanuel Mazer, and Christine Bellier

Subjects
Iterative method, business.industry, Computer science, Parallel manipulator, Control engineering, Robotics, Robot control, Control theory, Path (graph theory), Trajectory, Robot, Six degrees of freedom, Artificial intelligence, business
Abstract

This paper deals with the problem of planning safe trajectories for a manipulator robot moving in an environment including both static obstacles and objects (other robots, conveyors ...) whose positions may change during the interval of time separating two planning steps. In order to solve this problem for a general manipulator having six degrees of freedom, we have developed and implemented a method combining two different path planners: a local planner whose purpose is to take the manipulator away from the constrained initial and final configurations, and a global planner which is in charge of generating a safe trajectory for the first three degrees of freedom of the arm. Both the local and the global planners are based on a fast iterative algorithm for computing the distance between pairs of objects. These planners have been implemented within a robot programming system including a solid modeler dedicated to robotics applications and a connection to a robot controller.

Published
2006
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18. Merging probabilistic models of navigation: the Bayesian Map and the Superposition operator

Author

Pierre Bessière, Julien Diard, Emmanuel Mazer, Geometry and Probability for Motion and Action (E-MOTION), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, and Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
Superposition operator, 0209 industrial biotechnology, Computer science, business.industry, Bayesian probability, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Probabilistic logic, Mobile robot, 02 engineering and technology, Machine learning, computer.software_genre, Mobile robot navigation, Formalism (philosophy of mathematics), 020901 industrial engineering & automation, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, business, Algorithm, computer
Abstract

voir basilic : http://emotion.inrialpes.fr/bibemotion/2005/DBM05/; International audience; This paper deals with the probabilistic modeling of space, in the context of mobile robot navigation. We define a formalism called the Bayesian Map, which allows incremental building of models, thanks to the Superposition operator, which is a formally well-defined operator. Firstly, we present a syntactic version of this operator, and secondly, a version where the previously obtained model is enriched by experimental learning. In the resulting map, locations are the conjunction of underlying possible locations, which allows for more precise localization and more complex tasks. A theoretical example validates the concept, and hints at its usefulness for realistic robotic scenarios.

Published
2005

19. Hierarchies of probabilistic models of navigation: the Bayesian Map and the Abstraction operator

Author

Julien Diard, Emmanuel Mazer, Pierre Bessière, Geometry and Probability for Motion and Action (E-MOTION), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, and Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
0209 industrial biotechnology, Hierarchy, business.industry, Computer science, Divergence-from-randomness model, Bayesian probability, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Probabilistic logic, Statistical model, 02 engineering and technology, 020901 industrial engineering & automation, Probabilistic CTL, 0202 electrical engineering, electronic engineering, information engineering, Probability distribution, 020201 artificial intelligence & image processing, Bayesian programming, Artificial intelligence, business, Probabilistic relevance model
Abstract

voir basilic : http://emotion.inrialpes.fr/bibemotion/2004/DBM04/ address: New Orleans, LA (US); This paper presents a new method for probabilistic modeling of space, called the Bayesian Map formalism. It offers a generalization of some common approaches found in the literature, as it does not constrain the dependency structure of the probabilistic model. The formalism allows incremental building of hierarchies of models, by the use of the Abstraction operator. In the resulting hierarchy, localization in the high level model is based on probabilistic competition of the lower level models. Experimental results validate the concept, and hint at its usefulness for large scale scenarios.

Published
2004

20. Bayesian Robot Programming

Author

Pierre Bessière, Julien Diard, Olivier Lebeltel, Emmanuel Mazer, Geometry and Probability for Motion and Action (E-MOTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Inria Grenoble - Rhône-Alpes, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
0209 industrial biotechnology, Iterative and incremental development, business.industry, Computer science, Probabilistic logic, [SCCO.COMP]Cognitive science/Computer science, Robotics, 02 engineering and technology, Bayesian inference, Sensor fusion, Machine learning, computer.software_genre, Robot learning, 020901 industrial engineering & automation, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, Robot, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Functional reactive programming
Abstract

International audience; We propose a new method to program robots based on Bayesian inference and learning. It is called BRP for Bayesian Robot Programming. The capacities of this programming method are demonstrated through a succession of increasingly complex experiments. Starting from the learning of simple reactive behaviors, we present instances of behavior combinations, sensor fusion, hierarchical behavior composition, situation recognition and temporal sequencing. This series of experiments comprises the steps in the incremental development of a complex robot program. The advantages and drawbacks of BRP are discussed along with these different experiments and summed up as a conclusion. These different robotics programs may be seen as an illustration of probabilistic programming applicable whenever one must deal with problems based on uncertain or incomplete knowledge. The scope of possible applications is obviously much broader than robotics.

Published
2004
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21. Multi-sensor data fusion using Bayesian programming: An automotive application

Author

Emmanuel Mazer, Pierre Bessière, T. Fraichard, Christophe Coué, Automatic Programming and Decisional Systems in Robotics (SHARP), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria), and French programme La Route Automatisée (http://www.lara.prd.fr) - European project IST-1999-12224 'Sensing of Car Environment at Low Speed Driving' (http://www.carsense.org)

Subjects
0209 industrial biotechnology, Computer science, business.industry, Bayesian probability, Probabilistic logic, Automotive industry, [SCCO.COMP]Cognitive science/Computer science, Advanced driver assistance systems, 02 engineering and technology, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], Sensor fusion, Machine learning, computer.software_genre, Robot control, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 020901 industrial engineering & automation, Robustness (computer science), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Bayesian programming, Computer vision, Artificial intelligence, business, computer
Abstract

International audience; A prerequisite to the design of future Advanced Driver Assistance Systems for cars is a sensing system providing all the information required for high-level driving assistance tasks. Carsense is a European project whose purpose is to develop such a new sensing system. It will combine different sensors (laser, radar and video) and will rely on the fusion of the information coming from these sensors in order to achieve better accuracy, robustness and an increase of the information content. This paper demonstrates the interest of using probabilistic reasoning techniques to address this challenging multi-sensor data fusion problem. The approach used is called Bayesian Programming. It is a general approach based on an implementation of the Bayesian theory. It was introduced first to design robot control programs but its scope of application is much broader and it can be used whenever one has to deal with problems involving uncertain or incomplete knowledge.

Published
2003
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22. Parallel Motion Planning with the Ariadne's Clew Algorithm

Author

Emmanuel Mazer, El-Ghazali Talbi, T. Chatroux, Juan-Manuel Ahuactzin, Pierre Bessière, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), Institut National Polytechnique de Grenoble (INPG), and Bessiere, Pierre

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], 0209 industrial biotechnology, Time path, [INFO.INFO-RO] Computer Science [cs]/Operations Research [cs.RO], Computer science, [SCCO.COMP]Cognitive science/Computer science, Mobile robot, 02 engineering and technology, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], Planner, law.invention, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 020901 industrial engineering & automation, law, [SCCO.COMP] Cognitive science/Computer science, Parallel motion, Genetic algorithm, 0202 electrical engineering, electronic engineering, information engineering, Six degrees of freedom, 020201 artificial intelligence & image processing, Configuration space, computer, Algorithm, Robotic arm, computer.programming_language
Abstract

We describe an implementation of a real time path planner for a robot arm with six degrees of freedom moving among dynamical obstacles. The planner is based on a novel technique called the Ariadne's Clew Algorithm. A brief description of this algorithm and parallel implementation of it are presented. Finally we analyze experiments made with this planner.

Published
1993

23. The Ariadne's clew algorithm: Global planning with local methods

Author

Juan-Manuel Ahuactzin, Emmanuel Mazer, Pierre Bessière, El-Ghazali Talbi, Bessiere, Pierre, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), Institut National Polytechnique de Grenoble (INPG), EEC ESPRIT projects SUPERNODE II & PAPAGENA - CONACYT (Mexico), Laboratoire de Génie Informatique (LGI - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-IMAG, Departamento de Ingeniería En Sistemas Computacionales, Universidad de las Américas, A.K. Peters, K. Goldberg, D. Halperin, J.C. Latombe & R. Wilson, and K. Goldberg, D. Halperin, J.C. Latombe & R. Wilson

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], 0209 industrial biotechnology, [INFO.INFO-RO] Computer Science [cs]/Operations Research [cs.RO], Computer science, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Parallel manipulator, [SCCO.COMP]Cognitive science/Computer science, 02 engineering and technology, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], Robot control, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 020901 industrial engineering & automation, Search algorithm, [SCCO.COMP] Cognitive science/Computer science, Genetic algorithm, Path (graph theory), 0202 electrical engineering, electronic engineering, information engineering, Robot, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], 020201 artificial intelligence & image processing, Motion planning, Massively parallel, Algorithm
Abstract

The goal of the work described in this paper is to build a path planner able to drive a robot in a dynamic environment where the obstacles are moving. In order to do so, we propose a method, called "Ariadne's clew algorithm", to build a global path planner based on the combination of two local planning algorithms: an EXPLORE algorithm and a SEARCH algorithm. The purpose of the EXPLORE algorithm is to collect information about the environment with an increasingly fine resolution by placing landmarks in the searched space. The goal of the SEARCH algorithm is to opportunistically check if the target can be easily reached from any given placed landmark. The Ariadne's clew algorithm is shown to be very fast in most cases allowing planning in dynamic environments. Hence, it is shown to be complete, which means that it is sure to find a path when one exists. Finally, we describe a massively parallel implementation of this algorithm.

Published
1993

24. Parallel robot motion planning in a dynamic environment

Author

El-Ghazali Talbi, Juan-Manuel Ahuactzin, Emmanuel Mazer, and Pierre Bessière

Subjects
Reduction (complexity), Mathematical optimization, Optimization problem, Computer science, Parallel algorithm, Graph partition, Parallel manipulator, Distributed memory, Motion planning, Massively parallel
Abstract

In order to achieve fast motion planning for a robot we have chosen the genetic algorithms for the following reasons: they are well adapted to search for solutions in high dimensionality search space. The algorithm can be used without reduction of its efficiency for arms with more than six degree of freedom, they are very tolerant to the form of the function to optimize, for instance these functions do not need to be neither differentiable or continuous. They make no assumptions about the problem space that they are searching. We are using them to solve other optimization problems: graph partitioning, quadratic assignment, ... they are easy to implement on massively parallel distributed memory architectures. The parallel algorithm proposed achieve near-linear speed-up.

Published
1992
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25. Task-level planning of pick-and-place robot motions

Author

Joseph L. Jones, Patrick A. O'Donnell, Emmanuel Mazer, and Tomás Lozano-Pérez

Subjects
Robot kinematics, Personal robot, Social robot, General Computer Science, Articulated robot, Computer science, Robot, Control engineering, Mobile robot, Motion planning, Simulation, Robot control, Robot locomotion
Abstract

A task-level robot system named Handey, which is under development, is described. The current system is limited to pick-and-place operations, and it has successfully carried out dozens of such operations involving a variety of parts in relatively complex environments. The pick-and-place problem is described, and approximate approaches to the problem are examined. Heuristic motion planning in Handey is then discussed. >

Published
1989
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26. Planning/executing six d.o.f. robot motions in complex environments

Author

Christian Laugier, Jocelyne Troccaz, Emmanuel Mazer, and C. Bellier

Subjects
Computer science, business.industry, Iterative method, Mobile manipulator, Control engineering, Robotics, Robot control, Path (graph theory), Trajectory, Six degrees of freedom, Robot, Artificial intelligence, Manipulator, business
Abstract

This paper deals with the problem of planning safe trajectories for a manipulator robot moving in an environment including both static obstacles and objects (other robots, conveyors) whose positions may change during the interval of time separating two planning steps. In order to solve this problem for a general manipulator having six degrees of freedom, the authors have developed and implemented a method combining two different path planners: a local planner whose purpose is to take the manipulator away from the constrained initial and final configurations, and a global planner which is in charge of generating a safe trajectory for the first three degrees of freedom of the arm. Both the local and the global planners are based on a fast iterative algorithm for computing the distance between pairs of objects. These planners have been implemented within a robot programming system (called ACT) including a solid modeler dedicated to robotics applications and a connection to a robot controller. >

27. Using genetic algorithms for robot motion planning

Author

Pierre Bessière, Emmanuel Mazer, Juan-Manuel Ahuactzin, El-Ghazali Talbi, Laboratoire d'Informatique Fondamentale et d'Intelligence Artificielle (LIFIA), Institut National Polytechnique de Grenoble (INPG), Laboratoire de Génie Informatique (LGI - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-IMAG, CNRS, Consejo Nacional de Ciencia y Tecnologia (Mexico), projet ESPRIT 'Supernode 2', and Bessiere, Pierre

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], 0209 industrial biotechnology, Mathematical optimization, Optimization problem, Holonomic, Computer science, [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Parallel algorithm, parallel algorithms, [SCCO.COMP]Cognitive science/Computer science, Mobile robot, robot motion planning, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], 02 engineering and technology, Kinematics, genetic algorithms, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 020901 industrial engineering & automation, [SCCO.COMP] Cognitive science/Computer science, Genetic algorithm, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Robot, 020201 artificial intelligence & image processing, Motion planning
Abstract

We present an ongoing research work on robot motion planning using genetic algorithms. Our goal is to use this technique to build fast motion planners for robot with six or more degree of freedom. After a short review of the existing methods, we will introduce the genetic algorithms by showing how they can be used to solve the invers kinematic problem. In the second part of the paper, we show that the path planning problem can be expressed as an optimization problem and thus solved with a genetic algorithm. We illustrate the approach by building a path planner for a planar arm with two degree of freedom, then we demonstrate the validity of the method by planning paths for an holonomic mobile robot. Finally we describe an implementation of the selected genetic algorithm on a massively parallel machine and show that fast planning response is made possible by using this approach.

28. A Bayesian framework for robotic programming

Author

Julien Diard, Olivier Lebeltel, Emmanuel Mazer, Pierre Bessière, Laboratoire Leibniz (Leibniz - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Automatic Programming and Decisional Systems in Robotics (SHARP), Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble (GRAVIR - IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, and Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
0209 industrial biotechnology, Generic programming, Computer science, business.industry, Principle of maximum entropy, [SCCO.COMP]Cognitive science/Computer science, Mobile robot, 0102 computer and information sciences, 02 engineering and technology, Bayesian inference, Sensor fusion, 01 natural sciences, Inductive programming, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Computer Science::Robotics, 020901 industrial engineering & automation, 010201 computation theory & mathematics, Programming paradigm, Reactive programming, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Artificial intelligence, business
Abstract

We propose an original method for programming robots based on bayesian inference and learning. This method formally deals with problems of uncertainty and incomplete information that are inherent to the field. Indeed, the principal difficulties of robot programming comes from the unavoidable incompleteness of the models used. We present the formalism for describing a robotic task as well as the resolution methods. This formalism is inspired by the theory of probability, suggested by the physicist E. T. Jaynes: “Probability as Logic” [1]. Learning and maximum entropy principle translate incompleteness into uncertainty. Bayesian inference offers a formal framework for reasoning with this uncertainty. The main contribution of this paper is the definition of a generic system of robotic programming and its experimental application. We illustrate it by programming a surveillance task with a mobile robot: the Khepera. In order to do this, we use generic programming resources called “descriptions”. We show how to define and use these resources in an incremental way (reactive behaviors, sensor fusion, situation recognition and sequences of behaviors) within a systematic and unified framework.

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