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ISRR 2019: International Symposium of Robotics Research (Hanoi, Vietnam, October 6-10, 2019), Item does not contain fulltext, Robot motions in the presence of humans should not only be feasible and safe, but also conform to human preferences. This, however, requires user feedback on the robot's behavior. In this work, we propose a novel approach to leverage the user's brain signals as a feedback modality in order to decode the judgment of robot trajectories and rank them according to the user’s preferences. We show that brain signals measured using electroencephalography during observation of a robotic arm’s trajectory as well as in response to preference statements are informative regarding the user's target trajectory. Furthermore, we demonstrate that user feedback from brain signals can be used to reliably infer pairwise trajectory preferences as well as to retrieve the target trajectories of the user with a performance comparable to explicit behavioral feedback.

Existing Deep Learning (DL) frameworks typically do not provide ready-to-use solutions for robotics, where very specific learning, reasoning, and embodiment problems exist. Their relatively steep learning curve and the different methodologies employed by DL compared to traditional approaches, along with the high complexity of DL models, which often leads to the need of employing specialized hardware accelerators, further increase the effort and cost needed to employ DL models in robotics. Also, most of the existing DL methods follow a static inference paradigm, as inherited by the traditional computer vision pipelines, ignoring active perception, which can be employed to actively interact with the environment in order to increase perception accuracy. In this paper, we present the Open Deep Learning Toolkit for Robotics (OpenDR). OpenDR aims at developing an open, non-proprietary, efficient, and modular toolkit that can be easily used by robotics companies and research institutions to efficiently develop and deploy AI and cognition technologies to robotics applications, providing a solid step towards addressing the aforementioned challenges. We also detail the design choices, along with an abstract interface that was created to overcome these challenges. This interface can describe various robotic tasks, spanning beyond traditional DL cognition and inference, as known by existing frameworks, incorporating openness, homogeneity and robotics-oriented perception e.g., through active perception, as its core design principles., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Learning & Autonomous Control, Joining and Mechanical Behavior

Contains fulltext : 222466.pdf (publisher's version ) (Open Access), Brain signals represent a communication modality that can allow users of assistive robots to specify high-level goals, such as the object to fetch and deliver. In this paper, we consider a screen-free Brain-Computer Interface (BCI), where the robot highlights candidate objects in the environment using a laser pointer, and the user goal is decoded from the evoked responses in the electroencephalogram (EEG). Having the robot present stimuli in the environment allows for more direct commands than traditional BCIs that require the use of graphical user interfaces. Yet bypassing a screen entails less control over stimulus appearances. In realistic environments, this leads to heterogeneous brain responses for dissimilar objects-posing a challenge for reliable EEG classification. We model object instances as subclasses to train specialized classifiers in the Riemannian tangent space, each of which is regularized by incorporating data from other objects. In multiple experiments with a total of 19 healthy participants, we show that our approach not only increases classification performance but is also robust to both heterogeneous and homogeneous objects. While especially useful in the case of a screen-free BCI, our approach can naturally be applied to other experimental paradigms with potential subclass structure.

In this paper we present CMRNet, a realtime approach based on a Convolutional Neural Network (CNN) to localize an RGB image of a scene in a map built from LiDAR data. Our network is not trained in the working area, i. e., CMRNet does not learn the map. Instead it learns to match an image to the map. We validate our approach on the KITTI dataset, processing each frame independently without any tracking procedure. CMRNet achieves 0.27m and 1.07° median localization accuracy on the sequence 00 of the odometry dataset, starting from a rough pose estimate displaced up to 3.5m and 17°. To the best of our knowledge this is the first CNN-based approach that learns to match images from a monocular camera to a given, preexisting 3D LiDAR-map.

Vision-based localization systems rely on highly-textured areas for achieving an accurate pose estimation. However, most previous path planning strategies propose to select trajectories with minimum pose uncertainty by leveraging only the geometric structure of the scene, neglecting the photometric information (i.e, texture). Our planner exploits the scene’s visual appearance (i.e, the photometric information) in combination with its 3D geometry. Furthermore, we assume that we have no prior knowledge about the environment given, meaning that there is no pre-computed map or 3D geometry available. We introduce a novel approach to update the optimal plan on-the-fly, as new visual information is gathered. We demonstrate our approach with real and simulated Micro Aerial Vehicles (MAVs) that perform perception-aware path planning in real-time during exploration. We show significantly reduced pose uncertainty over trajectories planned without considering the perception of the robot.

Localization is a critical capability for many autonomous mobile robots. Implicit in all localization and place recognition techniques is the ability to decide whether a place is novel or familiar; the idea of “has this place been visited before?”. To make this decision accurately, localization systems typically require prior calibration or training on datasets from the specific type of operating environment; a requirement that is not practical in many environments or applications. One approach to this problem has been to remove the perfect data association requirement for place recognition systems by utilizing robust pose graphs. However, this approach requires a batch-based process and consequently does not solve the problem of real-time navigation in the interim before optimization. We present a novel technique which calibrates the localization system online as the robot moves through an environment, alleviating the need for prior calibration of a localization system. We leverage a robust pose graph optimization to determine a suitable model for our place recognition system which is sensor-invariant and does not require manually-labeled data for threshold tuning. The system continually refines place recognition parameters using sensory data and an optimized pose graph in order to increase the instantaneous precision of the location matches output by the underlying robust pose graph. We evaluate the system using the public New College dataset and a multi-sensor indoor office dataset. Our results indicate that the system produces superior mapping performance to a static place recognition model, and superior instantaneous place recognition performance to a robust pose graph technique.

After the incredible success of deep learning in the computer vision domain, there has been much interest in applying Convolutional Network (ConvNet) features in robotic fields such as visual navigation and SLAM. Unfortunately, there are fundamental differences and challenges involved. Computer vision datasets are very different in character to robotic camera data, real-time performance is essential, and performance priorities can be different. This paper comprehensively evaluates and compares the utility of three state-of-the-art ConvNets on the problems of particular relevance to navigation for robots; viewpoint-invariance and condition-invariance, and for the first time enables real-time place recognition performance using ConvNets with large maps by integrating a variety of existing (locality-sensitive hashing) and novel (semantic search space partitioning) optimization techniques. We present extensive experiments on four real world datasets cultivated to evaluate each of the specific challenges in place recognition. The results demonstrate that speed-ups of two orders of magnitude can be achieved with minimal accuracy degradation, enabling real-time performance. We confirm that networks trained for semantic place categorization also perform better at (specific) place recognition when faced with severe appearance changes and provide a reference for which networks and layers are optimal for different aspects of the place recognition problem.

This paper presents a novel vision-based underwater robotic system for the identification and control of Crown-Of-Thorns starfish (COTS) in coral reef environments. COTS have been identified as one of the most significant threats to Australia's Great Barrier Reef. These starfish literally eat coral, impacting large areas of reef and the marine ecosystem that depends on it. Evidence has suggested that land-based nutrient runoff has accelerated recent outbreaks of COTS requiring extensive use of divers to manually inject biological agents into the starfish in an attempt to control population numbers. Facilitating this control program using robotics is the goal of our research. In this paper we introduce a vision-based COTS detection and tracking system based on a Random Forest Classifier (RFC) trained on images from underwater footage. To track COTS with a moving camera, we embed the RFC in a particle filter detector and tracker where the predicted class probability of the RFC is used as an observation probability to weight the particles, and we use a sparse optical flow estimation for the prediction step of the filter. The system is experimentally evaluated in a realistic laboratory setup using a robotic arm that moves a camera at different speeds and heights over a range of real-size images of COTS in a reef environment.

Interest in the area of collaborative Unmanned Aerial Vehicles (UAVs) in a Multi-Agent System is growing to compliment the strengths and weaknesses of the human-machine relationship. To achieve effective management of multiple heterogeneous UAVs, the status model of the agents must be communicated to each other. This paper presents the effects on operator Cognitive Workload (CW), Situation Awareness (SA), trust and performance by increasing the autonomy capability transparency through text-based communication of the UAVs to the human agents. The results revealed a reduction in CW, increase in SA, increase in the Competence, Predictability and Reliability dimensions of trust, and the operator performance.

The problem of learning a map with a mobile robot has been intensively studied in the past and is usually referred to as the simultaneous localization and mapping (SLAM) problem. However, most existing solutions to the SLAM problem learn the maps from scratch and have no means for incorporating prior information. In this paper, we present a novel SLAM approach that achieves global consistency by utilizing publicly accessible aerial photographs as prior information. It inserts correspondences found between stereo and three-dimensional range data and the aerial images as constraints into a graph-based formulation of the SLAM problem. We evaluate our algorithm based on large real-world datasets acquired even in mixed in- and outdoor environments by comparing the global accuracy with state-of-the-art SLAM approaches and GPS. The experimental results demonstrate that the maps acquired with our method show increased global consistency., Artificial Intelligence & Integrated Computer Systems

Robust and reliable localization is a fundamental prerequisite for many applications of mobile robots. Although there exist many solutions to the localization problem, structurally symmetrical or featureless environments can prevent different locations from being distinguishable given the data obtained with the robot’s sensors. Such ambiguities typically make localization approaches more likely to fail. In this paper, we investigate how artificial landmarks can be utilized to reduce the ambiguity in the environment. We present a practical approach to compute a configuration of indistinguishable landmarks that decreases the overall ambiguity and thus increases the robustness of the localization process. We evaluate our approach in different environments based on real data and in simulation. Our results demonstrate that our approach improves the localization performance of the robot and outperforms other landmark selection approaches., Artificial Intelligence & Integrated Computer Systems

The received signal strength (RSS) of radiofrequency signals emitted from beacons placed at known locations in an environment, can be used by a local positioning system (LPS) to estimate the location of a person or a mobile object. In indoor environments, interference, multipath propagation of RF signals, and the presence of obstacles and people, lead to a complex spatial distribution of the RSS, which is inaccurately described by simple parametric models. In this work, we present a Bayesian method for an indoor RFID location system which uses an observation model based in Gaussian processes (GPs) nonparametric regression to represent the environment-specific RSS distributions for the individual RFID tags. The experimental results in an indoor environment demonstrate the effectiveness of GPs in order to increase positioning accuracy.

In this paper, we address the problem of creating an objective benchmark for comparing SLAM approaches. We propose a framework for analyzing the results of SLAM approaches based on a metric for measuring the error of the corrected trajectory. The metric uses only relative relations between poses and does not rely on a global reference frame. The idea is related to graph-based SLAM approaches, namely to consider the energy that is needed to deform the trajectory estimated by a SLAM approach into the ground truth trajectory. Our method enables us to compare SLAM approaches that use different estimation techniques or different sensor modalities since all computations are made based on the corrected trajectory of the robot. We provide sets of relative relations needed to compute our metric for an extensive set of datasets frequently used in the SLAM community. The relations have been obtained by manually matching laser-range observations to avoid the errors caused by matching algorithms. Our benchmark framework allows the user an easy analysis and objective comparisons between different SLAM approaches., Artificial Intelligence & Integrated Computer Systems

To effectively navigate in their environments and accurately reach their target locations, mobile robots require a globally consistent map of the environment. The problem of learning a map with a mobile robot has been intensively studied in the past and is usually referred to as the simultaneous localization and mapping (SLAM) problem. However, existing solutions to the SLAM problem typically rely on loop-closures to obtain global consistency and do not exploit prior information even if it is available. In this paper, we present a novel SLAM approach that achieves global consistency by utilizing publicly accessible aerial photographs as prior information. Our approach inserts correspondences found between three-dimensional laser range scans and the aerial image as constraints into a graph-based formulation of the SLAM problem. We evaluate our algorithm based on large real-world datasets acquired in a mixed in- and outdoor environment by comparing the global accuracy with state-of-the-art SLAM approaches and GPS. The experimental results demonstrate that the maps acquired with our method show increased global consistency., Artificial Intelligence & Integrated Computer Systems

We present an approach for creating conceptual representations of human-made indoor environments using mobile robots. The concepts refer to spatial and functional properties of typical indoor environments. Following different findings in spatial cognition, our model is composed of layers representing maps at different levels of abstraction. The complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition. The system also incorporates a linguistic framework that actively supports the map acquisition process, and which is used for situated dialogue. Finally, we discuss the capabilities of the integrated system., QC 20110823. IROS Workshop on From Sensors to Human Spatial Concepts 2007 Location: San Diego, CA Date: NOV, 2007

A major challenge in robotics and artificial intelligence lies in creating robots that are to cooperate with people in human-populated environments, e.g. for domestic assistance or elderly care. Such robots need skills that allow them to interact with the world and the humans living and working therein. In this paper we investigate the question of spatial understanding of human-made environments. The functionalities of our system comprise perception of the world, natural language, learning, and reasoning. For this purpose we integrate state-of-the-art components from different disciplines in AI, robotics and cognitive systems into a mobile robot system. The work focuses on the description of the principles we used for the integration, including cross-modal integration, ontology-based mediation, and multiple levels of abstraction of perception. Finally, we present experiments with the integrated “CoSy Explorer ” 1 system and list some of the major lessons that were learned from its design, implementation, and evaluation., QC 20111221

Indoor environments can typically be divided into places with different functionalities like corridors, rooms or doorways. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating the interaction with humans. As an example, natural language terms like ``corridor" or ``room" can be used to communicate the position of the robot in a map in a more intuitive way. In this work, we first propose an approach based on supervised learning to classify the pose of a mobile robot into semantic classes. Our method uses AdaBoost to boost simple features extracted from sensor range data into a strong classifier. We present two main applications of this approach. Firstly, we show how our approach can be utilized by a moving robot for an online classification of the poses traversed along its path using a hidden Markov model. In this case we additionally use as features objects extracted from images. Secondly, we introduce an approach to learn topological maps from geometric maps by applying our semantic classification procedure in combination with a probabilistic relaxation method. Alternatively, we apply associative Markov networks to classify geometric maps and compare the results with the relaxation approach. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various indoor environments, QC 20120113