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1. Multi-objective tuning for torque PD controllers of cobots

Author

Navarro-Cabrera, Diego, Luque, Niceto R., and Ros, Eduardo

Subjects
Computer Science - Robotics
Abstract

Collaborative robotics is a new and challenging field in the realm of motion control and human-robot interaction. The safety measures needed for a reliable interaction between the robot and its environment hinder the use of classical control methods, pushing researchers to try new techniques such as machine learning (ML). In this context, reinforcement learning has been adopted as the primary way to create intelligent controllers for collaborative robots, however supervised learning shows great promise in the hope of developing data-driven model based ML controllers in a faster and safer way. In this work we study several aspects of the methodology needed to create a dataset to be used to learn the dynamics of a robot. For this we tune several PD controllers to several trajectories, using a multi-objective genetic algorithm (GA) which takes into account not only their accuracy, but also their safety. We demonstrate the need to tune the controllers individually to each trajectory and empirically explore the best population size for the GA and how the speed of the trajectory affects the tuning and the dynamics of the robot., Comment: Accepted for presentation at the CPS workshop 2023

Published
2023

2. A Metric for Evaluating Neural Input Representation in Supervised Learning Networks

Author

Carrillo, Richard R, Naveros, Francisco, Ros, Eduardo, and Luque, Niceto R

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

Supervised learning has long been attributed to several feed-forward neural circuits within the brain, with attention being paid to the cerebellar granular layer. The focus of this study is to evaluate the input activity representation of these feed-forward neural networks. The activity of cerebellar granule cells is conveyed by parallel fibers and translated into Purkinje cell activity; the sole output of the cerebellar cortex. The learning process at this parallel-fiber-to-Purkinje-cell connection makes each Purkinje cell sensitive to a set of specific cerebellar states, determined by the granule-cell activity during a certain time window. A Purkinje cell becomes sensitive to each neural input state and, consequently, the network operates as a function able to generate a desired output for each provided input by means of supervised learning. However, not all sets of Purkinje cell responses can be assigned to any set of input states due to the network's own limitations (inherent to the network neurobiological substrate), that is, not all input-output mapping can be learned. A limiting factor is the representation of the input states through granule-cell activity. The quality of this representation will determine the capacity of the network to learn a varied set of outputs. In this study we present an algorithm for evaluating quantitatively the level of compatibility/interference amongst a set of given cerebellar states according to their representation (granule-cell activation patterns) without the need for actually conducting simulations and network training. The algorithm input consists of a real-number matrix that codifies the activity level of every considered granule-cell in each state. The capability of this representation to generate a varied set of outputs is evaluated geometrically, thus resulting in a real number that assesses the goodness of the representation

Published
2020
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3. Exploring vestibulo-ocular adaptation in a closed-loop neuro-robotic experiment using STDP. A simulation study

Author

Naveros, Francisco, Garrido, Jesus A., Arleo, Angelo, Ros, Eduardo, and Luque, Niceto R.

Subjects
Quantitative Biology - Neurons and Cognition, Computer Science - Neural and Evolutionary Computing, Computer Science - Robotics
Abstract

Studying and understanding the computational primitives of our neural system requires for a diverse and complementary set of techniques. In this work, we use the Neuro-robotic Platform (NRP)to evaluate the vestibulo ocular cerebellar adaptatIon (Vestibulo-ocular reflex, VOR)mediated by two STDP mechanisms located at the cerebellar molecular layer and the vestibular nuclei respectively. This simulation study adopts an experimental setup (rotatory VOR)widely used by neuroscientists to better understand the contribution of certain specific cerebellar properties (i.e. distributed STDP, neural properties, coding cerebellar topology, etc.)to r-VOR adaptation. The work proposes and describes an embodiment solution for which we endow a simulated humanoid robot (iCub)with a spiking cerebellar model by means of the NRP, and we face the humanoid to an r-VOR task. The results validate the adaptive capabilities of the spiking cerebellar model (with STDP)in a perception-action closed-loop (r- VOR)causing the simulated iCub robot to mimic a human behavior.

Published
2020
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4. VOR Adaptation on a Humanoid iCub Robot Using a Spiking Cerebellar Model

Author

Naveros, Francisco, Luque, Niceto R., Ros, Eduardo, and Arleo, Angelo

Subjects
Quantitative Biology - Neurons and Cognition, Computer Science - Neural and Evolutionary Computing, Computer Science - Robotics, Electrical Engineering and Systems Science - Systems and Control
Abstract

We embed a spiking cerebellar model within an adaptive real-time (RT) control loop that is able to operate a real robotic body (iCub) when performing different vestibulo-ocular reflex (VOR) tasks. The spiking neural network computation, including event- and time-driven neural dynamics, neural activity, and spike-timing dependent plasticity (STDP) mechanisms, leads to a nondeterministic computation time caused by the neural activity volleys encountered during cerebellar simulation. This nondeterministic computation time motivates the integration of an RT supervisor module that is able to ensure a well-orchestrated neural computation time and robot operation. Actually, our neurorobotic experimental setup (VOR) benefits from the biological sensory motor delay between the cerebellum and the body to buffer the computational overloads as well as providing flexibility in adjusting the neural computation time and RT operation. The RT supervisor module provides for incremental countermeasures that dynamically slow down or speed up the cerebellar simulation by either halting the simulation or disabling certain neural computation features (i.e., STDP mechanisms, spike propagation, and neural updates) to cope with the RT constraints imposed by the real robot operation. This neurorobotic experimental setup is applied to different horizontal and vertical VOR adaptive tasks that are widely used by the neuroscientific community to address cerebellar functioning. We aim to elucidate the manner in which the combination of the cerebellar neural substrate and the distributed plasticity shapes the cerebellar neural activity to mediate motor adaptation. This paper underlies the need for a two-stage learning process to facilitate VOR acquisition.

Published
2020
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5. On robot compliance. A cerebellar control approach

Author

Abadia, Ignacio, Naveros, Francisco, Garrido, Jesus A., Ros, Eduardo, and Luque, Niceto R.

Subjects
Computer Science - Robotics, Computer Science - Neural and Evolutionary Computing, Electrical Engineering and Systems Science - Systems and Control
Abstract

The work presented here is a novel biological approach for the compliant control of a robotic arm in real time (RT). We integrate a spiking cerebellar network at the core of a feedback control loop performing torque-driven control. The spiking cerebellar controller provides torque commands allowing for accurate and coordinated arm movements. To compute these output motor commands, the spiking cerebellar controller receives the robot's sensorial signals, the robot's goal behavior, and an instructive signal. These input signals are translated into a set of evolving spiking patterns representing univocally a specific system state at every point of time. Spike-timing-dependent plasticity (STDP) is then supported, allowing for building adaptive control. The spiking cerebellar controller continuously adapts the torque commands provided to the robot from experience as STDP is deployed. Adaptive torque commands, in turn, help the spiking cerebellar controller to cope with built-in elastic elements within the robot's actuators mimicking human muscles (inherently elastic). We propose a natural integration of a bio inspired control scheme, based on the cerebellum, with a compliant robot. We prove that our compliant approach outperforms the accuracy of the default factory-installed position control in a set of tasks used for addressing cerebellar motor behavior: controlling six degrees of freedom (DoF) in smooth movements, fast ballistic movements, and unstructured scenario compliant movements.

Published
2020
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11. Event and Time Driven Hybrid Simulation of Spiking Neural Networks

Author

Garrido, Jesus A., Carrillo, Richard R., Luque, Niceto R., Ros, Eduardo, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Cabestany, Joan, editor, Rojas, Ignacio, editor, and Joya, Gonzalo, editor

Published
2011
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12. Context Separability Mediated by the Granular Layer in a Spiking Cerebellum Model for Robot Control

Author

Luque, Niceto R., Garrido, Jesús A., Carrillo, Richard R., Ros, Eduardo, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Cabestany, Joan, editor, Rojas, Ignacio, editor, and Joya, Gonzalo, editor

Published
2011
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16. A computational model of homeostatic cerebellar compensation of ageing in vestibulo-ocular reflex adaptation

Author

Luque, Niceto R., Naveros, Francisco, Ros, Eduardo, Arleo, Angelo, Institut de la Vision, Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Department of Computer Architecture and Technology, University of Granada (CITIC), Spain, Department of Computer Architecture and Technology, University of Granada (CITIC), ARLEO, Angelo, Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and Universidad de Granada = University of Granada (UGR)

Subjects
[SDV] Life Sciences [q-bio], genetic structures, [SDV]Life Sciences [q-bio], sense organs
Abstract

The vestibulo-ocular reflex (VOR) stabilises vision during head motion. Age-related structural changes predict a linear VOR decay, whereas epidemiological data show a non-linear temporal profile. Here, we model cerebellar-dependent VOR adaptation to link structural and functional changes throughout ageing. We posit that three neurosynaptic factors codetermine VOR ageing patterns: electrical coupling between inferior olive neurons, intrinsic plasticity at Purkinje cell synapses, and long-term spike timing dependent plasticity at parallel fibre - Purkinje cell synapses as well as mossy fibre - medial vestibular nuclei synapses. Our cross-sectional simulations show that long-term plasticity acts as a global homeostatic mechanism mediating the non-linear temporal profile of VOR. Our results also suggest that intrinsic plasticity at Purkinje cells acts as a local homeostatic mechanism sustaining VOR at old ages. Importantly, longitudinal simulations show that residual fibres coding for the peak and trough of the VOR cycle constitute a predictive hallmark of VOR ageing trajectories.

Published
2020

22. Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation

Author

Luque, Niceto R., Naveros, Francisco, Carrillo, Richard R., Ros, Eduardo, Arleo, Angelo, Institut de la Vision, Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM), University of Granada [Granada], Gestionnaire, Hal Sorbonne Université, Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and Universidad de Granada = University of Granada (UGR)

Subjects
Eye Movements, genetic structures, Physiology, Visual System, QH301-705.5, [SDV]Life Sciences [q-bio], [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, Sensory Physiology, Long-Term Potentiation, Action Potentials, Neurophysiology, Membrane Potential, Nervous System, Purkinje Cells, Developmental Neuroscience, Animal Cells, Ocular System, Cerebellum, Medicine and Health Sciences, Animals, Humans, Learning, Biology (General), Neurons, Cerebral Cortex, [SDV.NEU.NB] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, Biology and Life Sciences, Brain, Cell Biology, Reflex, Vestibulo-Ocular, Adaptation, Physiological, Sensory Systems, Electrophysiology, Cellular Neuroscience, Synapses, Eyes, sense organs, Cellular Types, Anatomy, Head, Research Article, Neuroscience, Synaptic Plasticity
Abstract

Cerebellar Purkinje cells mediate accurate eye movement coordination. However, it remains unclear how oculomotor adaptation depends on the interplay between the characteristic Purkinje cell response patterns, namely tonic, bursting, and spike pauses. Here, a spiking cerebellar model assesses the role of Purkinje cell firing patterns in vestibular ocular reflex (VOR) adaptation. The model captures the cerebellar microcircuit properties and it incorporates spike-based synaptic plasticity at multiple cerebellar sites. A detailed Purkinje cell model reproduces the three spike-firing patterns that are shown to regulate the cerebellar output. Our results suggest that pauses following Purkinje complex spikes (bursts) encode transient disinhibition of target medial vestibular nuclei, critically gating the vestibular signals conveyed by mossy fibres. This gating mechanism accounts for early and coarse VOR acquisition, prior to the late reflex consolidation. In addition, properly timed and sized Purkinje cell bursts allow the ratio between long-term depression and potentiation (LTD/LTP) to be finely shaped at mossy fibre-medial vestibular nuclei synapses, which optimises VOR consolidation. Tonic Purkinje cell firing maintains the consolidated VOR through time. Importantly, pauses are crucial to facilitate VOR phase-reversal learning, by reshaping previously learnt synaptic weight distributions. Altogether, these results predict that Purkinje spike burst-pause dynamics are instrumental to VOR learning and reversal adaptation., Author summary Cerebellar Purkinje cells regulate accurate eye movement coordination. However, it remains unclear how cerebellar-dependent oculomotor adaptation depends on the interplay between Purkinje cell characteristic response patterns: tonic, high frequency bursting, and post-complex spike pauses. We explore the role of Purkinje spike burst-pause dynamics in VOR adaptation. A biophysical model of Purkinje cell is at the core of a spiking network model, which captures the cerebellar microcircuit properties and incorporates spike-based synaptic plasticity mechanisms at different cerebellar sites. We show that Purkinje spike burst-pause dynamics are critical for (1) gating the vestibular-motor response association during VOR acquisition; (2) mediating the LTD/LTP balance for VOR consolidation; (3) reshaping synaptic efficacy distributions for VOR phase-reversal adaptation; (4) explaining the reversal VOR gain discontinuities during sleeping.

Published
2019
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27. 26th Annual Computational Neuroscience Meeting (CNS*2017): Part 2

Author

Rubchinsky, Leonid L., primary, Ahn, Sungwoo, additional, Klijn, Wouter, additional, Cumming, Ben, additional, Yates, Stuart, additional, Karakasis, Vasileios, additional, Peyser, Alexander, additional, Woodman, Marmaduke, additional, Diaz-Pier, Sandra, additional, Deraeve, James, additional, Vassena, Eliana, additional, Alexander, William, additional, Beeman, David, additional, Kudela, Pawel, additional, Boatman-Reich, Dana, additional, Anderson, William S., additional, Luque, Niceto R., additional, Naveros, Francisco, additional, Carrillo, Richard R., additional, Ros, Eduardo, additional, Arleo, Angelo, additional, Huth, Jacob, additional, Ichinose, Koki, additional, Park, Jihoon, additional, Kawai, Yuji, additional, Suzuki, Junichi, additional, Mori, Hiroki, additional, Asada, Minoru, additional, Oprisan, Sorinel A., additional, Dave, Austin I., additional, Babaie, Tahereh, additional, Robinson, Peter, additional, Tabas, Alejandro, additional, Andermann, Martin, additional, Rupp, André, additional, Balaguer-Ballester, Emili, additional, Lindén, Henrik, additional, Christensen, Rasmus K., additional, Nakamura, Mari, additional, Barkat, Tania R., additional, Tosi, Zach, additional, Beggs, John, additional, Lonardoni, Davide, additional, Boi, Fabio, additional, Di Marco, Stefano, additional, Maccione, Alessandro, additional, Berdondini, Luca, additional, Jędrzejewska-Szmek, Joanna, additional, Dorman, Daniel B., additional, Blackwell, Kim T., additional, Bauermeister, Christoph, additional, Keren, Hanna, additional, Braun, Jochen, additional, Dornas, João V., additional, Mavritsaki, Eirini, additional, Aldrovandi, Silvio, additional, Bridger, Emma, additional, Lim, Sukbin, additional, Brunel, Nicolas, additional, Buchin, Anatoly, additional, Kerr, Clifford Charles, additional, Chizhov, Anton, additional, Huberfeld, Gilles, additional, Miles, Richard, additional, Gutkin, Boris, additional, Spencer, Martin J., additional, Meffin, Hamish, additional, Grayden, David B., additional, Burkitt, Anthony N., additional, Davey, Catherine E., additional, Tao, Liangyu, additional, Tiruvadi, Vineet, additional, Ali, Rehman, additional, Mayberg, Helen, additional, Butera, Robert, additional, Gunay, Cengiz, additional, Lamb, Damon, additional, Calabrese, Ronald L., additional, Doloc-Mihu, Anca, additional, López-Madrona, Víctor J., additional, Matias, Fernanda S., additional, Pereda, Ernesto, additional, Mirasso, Claudio R., additional, Canals, Santiago, additional, Geminiani, Alice, additional, Pedrocchi, Alessandra, additional, D’Angelo, Egidio, additional, Casellato, Claudia, additional, Chauhan, Ankur, additional, Soman, Karthik, additional, Srinivasa Chakravarthy, V., additional, Muddapu, Vignayanandam R., additional, Chuang, Chao-Chun, additional, Chen, Nan-yow, additional, Bayati, Mehdi, additional, Melchior, Jan, additional, Wiskott, Laurenz, additional, Azizi, Amir Hossein, additional, Diba, Kamran, additional, Cheng, Sen, additional, Smirnova, Elena Y., additional, Yakimova, Elena G., additional, Chizhov, Anton V., additional, Chen, Nan-Yow, additional, Shih, Chi-Tin, additional, Florescu, Dorian, additional, Coca, Daniel, additional, Courtiol, Julie, additional, Jirsa, Viktor K., additional, Covolan, Roberto J. M., additional, Teleńczuk, Bartosz, additional, Kempter, Richard, additional, Curio, Gabriel, additional, Destexhe, Alain, additional, Parker, Jessica, additional, Klishko, Alexander N., additional, Prilutsky, Boris I., additional, Cymbalyuk, Gennady, additional, Franke, Felix, additional, Hierlemann, Andreas, additional, da Silveira, Rava Azeredo, additional, Casali, Stefano, additional, Masoli, Stefano, additional, Rizza, Martina, additional, Rizza, Martina Francesca, additional, Sun, Yinming, additional, Wong, Willy, additional, Farzan, Faranak, additional, Blumberger, Daniel M., additional, Daskalakis, Zafiris J., additional, Popovych, Svitlana, additional, Viswanathan, Shivakumar, additional, Rosjat, Nils, additional, Grefkes, Christian, additional, Daun, Silvia, additional, Gentiletti, Damiano, additional, Suffczynski, Piotr, additional, Gnatkovski, Vadym, additional, De Curtis, Marco, additional, Lee, Hyeonsu, additional, Paik, Se-Bum, additional, Choi, Woochul, additional, Jang, Jaeson, additional, Park, Youngjin, additional, Song, Jun Ho, additional, Song, Min, additional, Pallarés, Vicente, additional, Gilson, Matthieu, additional, Kühn, Simone, additional, Insabato, Andrea, additional, Deco, Gustavo, additional, Glomb, Katharina, additional, Ponce-Alvarez, Adrián, additional, Ritter, Petra, additional, Campo, Adria Tauste, additional, Thiele, Alexander, additional, Deeba, Farah, additional, Robinson, P. A., additional, van Albada, Sacha J., additional, Rowley, Andrew, additional, Hopkins, Michael, additional, Schmidt, Maximilian, additional, Stokes, Alan B., additional, Lester, David R., additional, Furber, Steve, additional, Diesmann, Markus, additional, Barri, Alessandro, additional, Wiechert, Martin T., additional, DiGregorio, David A., additional, Dimitrov, Alexander G., additional, Vich, Catalina, additional, Berg, Rune W., additional, Guillamon, Antoni, additional, Ditlevsen, Susanne, additional, Cazé, Romain D., additional, Girard, Benoît, additional, Doncieux, Stéphane, additional, Doyon, Nicolas, additional, Boahen, Frank, additional, Desrosiers, Patrick, additional, Laurence, Edward, additional, Dubé, Louis J., additional, Eleonora, Russo, additional, Durstewitz, Daniel, additional, Schmidt, Dominik, additional, Mäki-Marttunen, Tuomo, additional, Krull, Florian, additional, Bettella, Francesco, additional, Metzner, Christoph, additional, Devor, Anna, additional, Djurovic, Srdjan, additional, Dale, Anders M., additional, Andreassen, Ole A., additional, Einevoll, Gaute T., additional, Næss, Solveig, additional, Ness, Torbjørn V., additional, Halnes, Geir, additional, Halgren, Eric, additional, Pettersen, Klas H., additional, Sætra, Marte J., additional, Hagen, Espen, additional, Schiffer, Alina, additional, Grzymisch, Axel, additional, Persike, Malte, additional, Ernst, Udo, additional, Harnack, Daniel, additional, Ernst, Udo A., additional, Tomen, Nergis, additional, Zucca, Stefano, additional, Pasquale, Valentina, additional, Pica, Giuseppe, additional, Molano-Mazón, Manuel, additional, Chiappalone, Michela, additional, Panzeri, Stefano, additional, Fellin, Tommaso, additional, Oie, Kelvin S., additional, Boothe, David L., additional, Crone, Joshua C., additional, Yu, Alfred B., additional, Felton, Melvin A., additional, Zulfiqar, Isma, additional, Moerel, Michelle, additional, De Weerd, Peter, additional, Formisano, Elia, additional, Oie, Kelvin, additional, Franaszczuk, Piotr, additional, Diggelmann, Roland, additional, Fiscella, Michele, additional, Guarino, Domenico, additional, Antolík, Jan, additional, Davison, Andrew P., additional, Frègnac, Yves, additional, Etienne, Benjamin Xavier, additional, Frohlich, Flavio, additional, Lefebvre, Jérémie, additional, Marcos, Encarni, additional, Mattia, Maurizio, additional, Genovesio, Aldo, additional, Fedorov, Leonid A., additional, Dijkstra, Tjeerd M.H., additional, Sting, Louisa, additional, Hock, Howard, additional, Giese, Martin A., additional, Buhry, Laure, additional, Langlet, Clément, additional, Giovannini, Francesco, additional, Verbist, Christophe, additional, Salvadé, Stefano, additional, Giugliano, Michele, additional, Henderson, James A., additional, Wernecke, Hendrik, additional, Sándor, Bulcsú, additional, Gros, Claudius, additional, Voges, Nicole, additional, Dabrovska, Paulina, additional, Riehle, Alexa, additional, Brochier, Thomas, additional, Grün, Sonja, additional, Gu, Yifan, additional, Gong, Pulin, additional, Dumont, Grégory, additional, Novikov, Nikita A., additional, Gutkin, Boris S., additional, Tewatia, Parul, additional, Eriksson, Olivia, additional, Kramer, Andrei, additional, Santos, Joao, additional, Jauhiainen, Alexandra, additional, Kotaleski, Jeanette H., additional, Belić, Jovana J., additional, Kumar, Arvind, additional, Kotaleski, Jeanette Hellgren, additional, Shimono, Masanori, additional, Hatano, Naomichi, additional, Ahmad, Subutai, additional, Cui, Yuwei, additional, Hawkins, Jeff, additional, Senk, Johanna, additional, Korvasová, Karolína, additional, Tetzlaff, Tom, additional, Helias, Moritz, additional, Kühn, Tobias, additional, Denker, Michael, additional, Mana, PierGianLuca, additional, Dahmen, David, additional, Schuecker, Jannis, additional, Goedeke, Sven, additional, Keup, Christian, additional, Heuer, Katja, additional, Bakker, Rembrandt, additional, Tiesinga, Paul, additional, Toro, Roberto, additional, Qin, Wei, additional, Hadjinicolaou, Alex, additional, Ibbotson, Michael R., additional, Kameneva, Tatiana, additional, Lytton, William W., additional, Mulugeta, Lealem, additional, Drach, Andrew, additional, Myers, Jerry G., additional, Horner, Marc, additional, Vadigepalli, Rajanikanth, additional, Morrison, Tina, additional, Walton, Marlei, additional, Steele, Martin, additional, Anthony Hunt, C., additional, Tam, Nicoladie, additional, Amaducci, Rodrigo, additional, Muñiz, Carlos, additional, Reyes-Sánchez, Manuel, additional, Rodríguez, Francisco B., additional, Varona, Pablo, additional, Cronin, Joseph T., additional, Hennig, Matthias H., additional, Iavarone, Elisabetta, additional, Yi, Jane, additional, Shi, Ying, additional, Zandt, Bas-Jan, additional, Van Geit, Werner, additional, Rössert, Christian, additional, Markram, Henry, additional, Hill, Sean, additional, O’Reilly, Christian, additional, Perin, Rodrigo, additional, Lu, Huanxiang, additional, Bryson, Alexander, additional, Hadrava, Michal, additional, Hlinka, Jaroslav, additional, Hosaka, Ryosuke, additional, Olenik, Mark, additional, Houghton, Conor, additional, Iannella, Nicolangelo, additional, Launey, Thomas, additional, Kotsakidis, Rebecca, additional, Soriano, Jaymar, additional, Kubo, Takatomi, additional, Inoue, Takao, additional, Kida, Hiroyuki, additional, Yamakawa, Toshitaka, additional, Suzuki, Michiyasu, additional, Ikeda, Kazushi, additional, Abbasi, Samira, additional, Hudson, Amber E., additional, Heck, Detlef H., additional, Jaeger, Dieter, additional, Lee, Joel, additional, Janušonis, Skirmantas, additional, Saggio, Maria Luisa, additional, Spiegler, Andreas, additional, Stacey, William C., additional, Bernard, Christophe, additional, Lillo, Davide, additional, Petkoski, Spase, additional, Drakesmith, Mark, additional, Jones, Derek K., additional, Zadeh, Ali Sadegh, additional, Kambhampati, Chandra, additional, Karbowski, Jan, additional, Kaya, Zeynep Gokcen, additional, Lakretz, Yair, additional, Treves, Alessandro, additional, Li, Lily W., additional, Lizier, Joseph, additional, Kerr, Cliff C., additional, Masquelier, Timothée, additional, Kheradpisheh, Saeed Reza, additional, Kim, Hojeong, additional, Kim, Chang Sub, additional, Marakshina, Julia A., additional, Vartanov, Alexander V., additional, Neklyudova, Anastasia A., additional, Kozlovskiy, Stanislav A., additional, Kiselnikov, Andrey A., additional, Taniguchi, Kanako, additional, Kitano, Katsunori, additional, Schmitt, Oliver, additional, Lessmann, Felix, additional, Schwanke, Sebastian, additional, Eipert, Peter, additional, Meinhardt, Jennifer, additional, Beier, Julia, additional, Kadir, Kanar, additional, Karnitzki, Adrian, additional, Sellner, Linda, additional, Klünker, Ann-Christin, additional, Kuch, Lena, additional, Ruß, Frauke, additional, Jenssen, Jörg, additional, Wree, Andreas, additional, Sanz-Leon, Paula, additional, Knock, Stuart A., additional, Chien, Shih-Cheng, additional, Maess, Burkhard, additional, Knösche, Thomas R., additional, Cohen, Charles C., additional, Popovic, Marko A., additional, Klooster, Jan, additional, Kole, Maarten H.P., additional, Roberts, Erik A., additional, Kopell, Nancy J., additional, Kepple, Daniel, additional, Giaffar, Hamza, additional, Rinberg, Dima, additional, Koulakov, Alex, additional, Forlim, Caroline Garcia, additional, Klock, Leonie, additional, Bächle, Johanna, additional, Stoll, Laura, additional, Giemsa, Patrick, additional, Fuchs, Marie, additional, Schoofs, Nikola, additional, Montag, Christiane, additional, Gallinat, Jürgen, additional, Lee, Ray X., additional, Stephens, Greg J., additional, Kuhn, Bernd, additional, Tauffer, Luiz, additional, Isope, Philippe, additional, Inoue, Katsuma, additional, Ohmura, Yoshiyuki, additional, Yonekura, Shogo, additional, Kuniyoshi, Yasuo, additional, Jang, Hyun Jae, additional, Kwag, Jeehyun, additional, de Kamps, Marc, additional, Lai, Yi Ming, additional, dos Santos, Filipa, additional, Lam, K. P., additional, Andras, Peter, additional, Imperatore, Julia, additional, Helms, Jessica, additional, Tompa, Tamas, additional, Lavin, Antonieta, additional, Inkpen, Felicity H., additional, Ashby, Michael C., additional, Lepora, Nathan F., additional, Shifman, Aaron R., additional, Lewis, John E., additional, Zhang, Zhong, additional, Feng, Yeqian, additional, Tetzlaff, Christian, additional, Kulvicius, Tomas, additional, Li, Yinyun, additional, Pena, Rodrigo F. O., additional, Bernardi, Davide, additional, Roque, Antonio C., additional, Lindner, Benjamin, additional, Vellmer, Sebastian, additional, Saudargiene, Ausra, additional, Maninen, Tiina, additional, Havela, Riikka, additional, Linne, Marja-Leena, additional, Powanwe, Arthur, additional, Longtin, Andre, additional, Garrido, Jesús A., additional, Graham, Joe W., additional, Dura-Bernal, Salvador, additional, Angulo, Sergio L., additional, Neymotin, Samuel A., additional, and Antic, Srdjan D., additional

Published
2017
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40. ADAPTIVE AND PREDICTIVE CONTROL OF A SIMULATED ROBOT ARM.

Author

TOLU, SILVIA, VANEGAS, MAURICIO, GARRIDO, JESÚS A., LUQUE, NICETO R., and ROS, EDUARDO

Subjects
ROBOTS, ROBOTICS, VELOCITY, CEREBELLAR cortex, ACCELERATION (Mechanics)
Abstract

In this work, a basic cerebellar neural layer and a machine learning engine are embedded in a recurrent loop which avoids dealing with the motor error or distal error problem. The presented approach learns the motor control based on available sensor error estimates (position, velocity, and acceleration) without explicitly knowing the motor errors. The paper focuses on how to decompose the input into different components in order to facilitate the learning process using an automatic incremental learning model (locally weighted projection regression (LWPR) algorithm). LWPR incrementally learns the forward model of the robot arm and provides the cerebellar module with optimal pre-processed signals. We present a recurrent adaptive control architecture in which an adaptive feedback (AF) controller guarantees a precise, compliant, and stable control during the manipulation of objects. Therefore, this approach efficiently integrates a bio-inspired module (cerebellar circuitry) with a machine learning component (LWPR). The cerebellar-LWPR synergy makes the robot adaptable to changing conditions. We evaluate how this scheme scales for robot-arms of a high number of degrees of freedom (DOFs) using a simulated model of a robot arm of the new generation of light weight robots (LWRs). [ABSTRACT FROM AUTHOR]

Published
2013
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41. Cerebellar Input Configuration Toward Object Model Abstraction in Manipulation Tasks.

Author

Luque, Niceto R., Garrido, Jesus A., Carrillo, Richard R., Coenen, Olivier J.-M. D., and Ros, Eduardo

Subjects
*CEREBELLUM, *TORQUE, *PHYSIOLOGICAL control systems, *ADAPTIVE control systems, *MICROPROCESSORS, *SENSORIMOTOR cortex, *ROBOT kinematics
Abstract

It is widely assumed that the cerebellum is one of the main nervous centers involved in correcting and refining planned movement and accounting for disturbances occurring during movement, for instance, due to the manipulation of objects which affect the kinematics and dynamics of the robot-arm plant model. In this brief, we evaluate a way in which a cerebellar-like structure can store a model in the granular and molecular layers. Furthermore, we study how its microstructure and input representations (context labels and sensorimotor signals) can efficiently support model abstraction toward delivering accurate corrective torque values for increasing precision during different-object manipulation. We also describe how the explicit (object-related input labels) and implicit state input representations (sensorimotor signals) complement each other to better handle different models and allow interpolation between two already stored models. This facilitates accurate corrections during manipulations of new objects taking advantage of already stored models. [ABSTRACT FROM AUTHOR]

Published
2011
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42. A neuromechanics solution for adjustable robot compliance and accuracy.

Author

Abadía I, Bruel A, Courtine G, Ijspeert AJ, Ros E, and Luque NR

Subjects
Humans, Biomechanical Phenomena, Equipment Design, Viscosity, Elasticity, Muscle Contraction physiology, Feedback, Computer Simulation, Movement physiology, Motion, Robotics instrumentation, Cerebellum physiology, Torque, Muscle, Skeletal physiology
Abstract

Robots have to adjust their motor behavior to changing environments and variable task requirements to successfully operate in the real world and physically interact with humans. Thus, robotics strives to enable a broad spectrum of adjustable motor behavior, aiming to mimic the human ability to function in unstructured scenarios. In humans, motor behavior arises from the integrative action of the central nervous system and body biomechanics; motion must be understood from a neuromechanics perspective. Nervous regions such as the cerebellum facilitate learning, adaptation, and coordination of our motor responses, ultimately driven by muscle activation. Muscles, in turn, self-stabilize motion through mechanical viscoelasticity. In addition, the agonist-antagonist arrangement of muscles surrounding joints enables cocontraction, which can be regulated to enhance motion accuracy and adapt joint stiffness, thereby providing impedance modulation and broadening the motor repertoire. Here, we propose a control solution that harnesses neuromechanics to enable adjustable robot motor behavior. Our solution integrates a muscle model that replicates mechanical viscoelasticity and cocontraction together with a cerebellar network providing motor adaptation. The resulting cerebello-muscular controller drives the robot through torque commands in a feedback control loop. Changes in cocontraction modify the muscle dynamics, and the cerebellum provides motor adaptation without relying on prior analytical solutions, driving the robot in different motor tasks, including payload perturbations and operation across unknown terrains. Experimental results show that cocontraction modulates robot stiffness, performance accuracy, and robustness against external perturbations. Through cocontraction modulation, our cerebello-muscular torque controller enables a broad spectrum of robot motor behavior.

Published
2025
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