Nonlinear Control
To date, most approaches to the control of nonlinear systems such as humanoid robots are highly dependent on handcrafted high gains and/or precise rigid body dynamics models. However, in order to ever leave laboratory floors, humanoid robots will require low-gain control so that they cannot damage their environment and due to the large extent of unmodeled nonlinearities the learning of the dynamics model will become essential. Over the last decades, we have developed several new approaches to nonlinear control and will illustrate some of these in more detail at this point.
Learning and Adaptive Control
Many difficult robot systems as well as other plants defy any attempt of modeling from a physical understanding. If high-gain control is impossible due to the application, compliance requirements or the usage of light-weight low-torque motors, then learning is often the only choice. In our lab, we have developed a variety of learning and adaptive control methods. Most of these techniques learn extremely fast and outperform human modeling by far in tested robot applications.
Gauss-Udwadia Control
Inspired by results from analytical dynamics, we have introduced a novel control architecture together with our collaborator Firdaus Udwadia (Department of Aerospace and Mechanical Engineering). This architecture allows the derivation both of novel as well as established control laws (e.g., operational space control laws) from a unique immediate cost optimal control perspective. We are currently working on a generalization which will allow the framework to become a learning framework.
Operational Space Control
We use nonlinear control techniques to address the issue of task achievement in operational space while maintaining coordination among redundant degrees of freedom: a particularly challenging problem for highly redundant robots, like humanoids. In addition to examining both traditional and novel redundancy resolution schemes on our 7-DOF manipulator, we are investigating operational space control techniques as a means of center-of-gravity placement for balancing legged platforms.
Contact persons: Jun Nakanishi, Michael Mistry, Jan Peters, Stefan Schaal
Publications
Evangelos A. Theodorou, Jonas Buchli, Stefan Schaal (submitted). Reinforcement Learning of Motor Skills in High Dimensions: A Pah Integral Approach. (MANUSCRIPT UNDER REVIEW. SUGGESTIONS WELCOME)).
[Keywords: reinforcement learning, stochastic optimal control]
[Detail] [BibTeX] [PDF]
Evangelos A. Theodorou, Yuval Tassa, Emo Todorov, (submitted). Stochastic Differential Dynamic Programming. (MANUSCRIPT UNDER REVIEW. SUGGESTIONS WELCOME).
[Keywords: stochastic differential dynamics programming,second order optimal control]
[Detail] [BibTeX] [PDF]
Evangelos A. Theodorou, Emo Todorov, Francisco Valero Cuevas (submitted). Stochastic Optimal Control on a Biologically Inspired Robotic Finger. (MANUSCRIPT UNDER REVIEW. SUGGESTIONS WELCOME).
[Keywords: stochastic optimal control, biomechanics]
[Detail] [BibTeX] [PDF]
Francisco J. Valero-Cuevas, Heiko Hoffmann, Manish U. Kurse, Jason J. Kutch, Evangelos A. Theodorou (in press). Computational models for neuromuscular function, IEEE REVIEWS IN BIOMEDICAL ENGINEERING, IN PRESS, OCTOBER 2009 (All authors have equally contributed).
[Keywords: modeling, biomechanics, neuromuscular,control,computational methods.]
[Detail] [BibTeX] [PDF]
Evangelos A. Theodorou.;Buchli, J.;Schaal, S. (2009). Path integral stochastic optimal control for rigid body dynamics, IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning (ADPRL2009).
[Keywords: reinforcement learning, optimal control, path integrals, stochastic systems]
[Detail] [BibTeX] [PDF]
Schaal, S. (2009). The SL simulation and real-time control software package, University of Southern California.
[Keywords: simulation environment, documentation, real-time control envirionment, motor control]
[Detail] [BibTeX] [PDF]
Hoffmann, H.;Theodorou, E.;Schaal, S. (2009). Human optimization strategies under reward feedback, Abstracts of Neural Control of Movement Conference (NCM 2009).
[Keywords: computational motor control, reinforcement leanring, human subjects]
[Detail] [BibTeX]
Buchli, J.; Kalakrishnan, M.; Mistry, M.; Pastor, P.; Schaal, S. (2009). Compliant Quadruped Locomotion Over Rough Terrain, IEEE/RSJ International Conference on Intelligent RObots and Systems.
[Keywords: quadruped locomotion, inverse dynamics, force control]
[Detail] [BibTeX] [PDF]
Evangelos A. Theodorou and Francisco J. Valero-Cuevas (2009). Incorporating Muscle Activation-Contraction dynamics to an optimal control framework for finger movements, Abstracts of Neural Control of Movement Conference (NCM 2009).
[Keywords: computational motor control,optimal control, biomechanics]
[Detail] [BibTeX] [PDF]
Ting, J. A.;D'Souza, A.;Yamamoto, K.;Yoshioka, T.;Hoffman, D.;Kakei, S.;Sergio, L.;Kalaska, J.;Kawato, M.;Strick, P.;Schaal, S. (2008). Variational Bayesian least squares: an application to brain-machine interface data, Neural Netw, 21, 8, pp.1112-31.
[Keywords: motor control, computational neuroscience, emg reconstruction, motor cortex, brain machine interfaces, bayesian least squares]
[Detail] [BibTeX] [PDF]
Theodorou, E.;Hoffmann, H.;Mistry, M.;Schaal, S. (2008). Computational model for movement learning under uncertain cost, Abstracts of the Society of Neuroscience Meeting (SFN 2008).
[Keywords: computational motor control, motor planning, optimization, reinforcement learning]
[Detail] [BibTeX]
Nakanishi, J.;Cory, R.;Mistry, M.;Peters, J.;Schaal, S. (2008). Operational space control: A theoretical and emprical comparison, International Journal of Robotics Research, 27, 6, pp.737-757.
[Keywords: task space control, operational space control, redundancy resolution, humanoid robotics]
[Detail] [BibTeX] [PDF]
Mistry, M.;Theodorou, E.;Hoffmann, H.;Schaal, S. (2008). The dual role of uncertainty in force field learning, Abstracts of the Eighteenth Annual Meeting of Neural Control of Movement (NCM).
[Keywords: computational motor control, optimal control, redundancy resolution, internal model]
[Detail] [BibTeX]
Hoffmann, H.;Theodorou, E.;Schaal, S. (2008). Behavioral experiments on reinforcement learning in human motor control, Abstracts of the Eighteenth Annual Meeting of Neural Control of Movement (NCM).
[Keywords: computational motor control, optimal control, reinforcement learning]
[Detail] [BibTeX]
Heiko Hoffmann, Evangelos Theodorou, and Stefan Schaal (2008). Optimization strategies in human reinforcement learning, Advances in Computational Motor Control VII, Symposium at the Society for Neuroscience Meeting, Washington DC, 2008.
[Keywords: reinforcement learning, motor control, psychophysics]
[Detail] [BibTeX] [PDF]
Peters, J.;Schaal, S. (2008). Learning to control in operational space, International Journal of Robotics Research, 27, pp.197-212.
[Keywords: operational space control, learning, em algorithm, redundancy resolution, reinforcement learning]
[Detail] [BibTeX] [PDF]
Mistry, M.;Theodorou, E.;Liaw, G.;Yoshioka, T.;Schaal, S.;Kawato, M. (2008). An investigation of optimality in reaching movements with an acceleration based force field, Abstracts of the Society of Neuroscience Meeting (SFN 2008).
[Keywords: computational motor control, motor planning, optimization, force fields]
[Detail] [BibTeX]
Hoffmann, H.;Schaal, S. (2008). Do humans plan continuous trajectories in kinematic coordinates?, Abstracts of the Society of Neuroscience Meeting (SFN 2008).
[Keywords: computational motor control, motor planning, representations, human subjects]
[Detail] [BibTeX]
Hoffmann, H.;Schaal, S. (2008). Human movement generation based on convergent flow fields: A computational model and a behavioral experiment, Advances in Computational Motor Control VII, Symposium at the Society for Neuroscience Meeting.
[Keywords: computational motor control, motor primitives, target switching]
[Detail] [BibTeX] [PDF]
Peters, J.;Mistry, M.;Udwadia, F. E.;Nakanishi, J.;Schaal, S. (2008). A unifying methodology for robot control with redundant DOFs, Autonomous Robots, 24, 1, pp.1-12.
[Keywords: operational space control, inverse control, dexterous manipulation, optimal control]
[Detail] [BibTeX] [PDF]
Mohajerian, P; Hoffmann, H.; Mistry, M.; Schaal, S. (2007). A Computational Model of Arm Trajectory Modification Using Dynamic Movement Primitives, Abstracts of the 37st Meeting of the Society of Neuroscience.
[Keywords: online movement correction, target switching, movement primitives, computational model, motor control]
[Detail] [BibTeX] [PDF]
Schaal, S; Mohajerian, P.; Ijspeert, A. (2007). Dynamics systems vs. optimal control — a unifying view, Progress in Brain Research, 165, pp.425-445.
[Keywords: discrete movement; rhythmic movement; movement primitives; dynamic systems; optimization; computational motor control]
[Detail] [BibTeX]
Theodorou, E; Peters, J; Schaal, S. (2007). Reinforcement Learning for Optimal Control of Arm Movements, Abstracts of the 37st Meeting of the Society of Neuroscience..
[Keywords: optimal control,reinforcement learning, arm movements]
[Detail] [BibTeX]
Mistry, M.;Nakanishi, J.;Schaal, S. (2007). Task space control with prioritization for balance and locomotion, IEEE International Conference on Intelligent Robotics Systems (IROS 2007).
[Keywords: operational space control, locomotion, balance, hierarchical]
[Detail] [BibTeX] [PDF]
Nakanishi, J.;Mistry, M.;Peters, J.;Schaal, S. (2007). Experimental evaluation of task space position/orientation control towards compliant control for humanoid robots, IEEE International Conference on Intelligent Robotics Systems (IROS 2007).
[Keywords: operational space control, quaternion, task space control, resolved motion rate control, resolved acceleration, force control]
[Detail] [BibTeX] [PDF]
Peters, J.;Schaal, S. (2007). Reinforcement learning for operational space control, International Conference on Robotics and Automation (ICRA2007), pp.2111-2116.
[Keywords: operational space control, reinforcement learning, weighted regression, em-algorithm]
[Detail] [BibTeX] [PDF]
Peters, J.;Schaal, S. (2007). Reinforcement learning by reward-weighted regression for operational space control, Proceedings of the International Conference on Machine Learning (ICML2007).
[Keywords: reinforcement learning, operational space control, weighted regression]
[Detail] [BibTeX] [PDF]
Pongas, D.;Mistry, M.;Schaal, S. (2007). A robust quadruped walking gait for traversing rough terrain, International Conference on Robotics and Automation (ICRA2007), pp.1474-1479.
[Keywords: quadruped locomotion, static walk, crawl gait, cog trajectory, rough terrain, internal-model control]
[Detail] [BibTeX] [PDF]
Hoffman, H.;Schaal, S. (2007). A computational model of human trajectory planning based on convergent flow fields, Abstracts of the 37st Meeting of the Society of Neuroscience.
[Keywords: motor control, computational neuroscience, behavior, motor primitives, force fields]
[Detail] [BibTeX]
Peters, J.;Schaal, S. (2006). Learning operational space control, in: Burgard, W.;Sukhatme, G. S.;Schaal, S. (eds.), Robotics: Science and Systems (RSS 2006), Cambridge, MA: MIT Press.
[Keywords: operational space control
redundancy
forward models
inverse models
compliance
reinforcement leanring
locally weighted learning]
[Detail] [BibTeX] [PDF]
Theodorou, E. (2006). Statistical Learning of LQG controllers, Technical Report-2006-1.
[Keywords: lqg controllers, statistical learning, system identification]
[Detail] [BibTeX] [PDF]
Nakanishi, J.;Cory, R.;Mistry, M.;Peters, J.;Schaal, S. (2005). Comparative experiments on task space control with redundancy resolution, IEEE International Conference on Intelligent Robots and Systems (IROS 2005), pp.3901-3908.
[Keywords: manipulator dynamics
redundant manipulators
space optimization
dynamical decoupling
humanoid robots
inverse kinematics
motor coordination
redundancy resolution
robot dynamics
seven-degree-of-freedom anthropomorphic robot arm
task space control
dynamical d]
[Detail] [BibTeX] [PDF]
Nakanishi, J.;Farrell, J. A.;Schaal, S. (2005). Composite adaptive control with locally weighted statistical learning, Neural Networks, 18, 1, pp.71-90.
[Keywords: adaptive control, statistical learning, composite control law, provably stable, locally weighted regression]
[Detail] [BibTeX] [PDF]
Shibata, T.;Tabata, H.;Schaal, S.;Kawato, M. (2005). A model of smooth pursuit based on learning of the target dynamics using only retinal signals, Neural Networks, 18, pp.213-225.
[Keywords: oculomotor control
learning
biomimetic robotics
internal models]
[Detail] [BibTeX] [PDF]
Mistry, M.;Mohajerian, P.;Schaal, S. (2005). Arm movement experiments with joint space force fields using an exoskeleton robot, IEEE Ninth International Conference on Rehabilitation Robotics, pp.408-413.
[Keywords: computational motor control
trajectory planning
inverse kinematics
force control
human motor control
torque perturbations]
[Detail] [BibTeX] [PDF]
Peters, J.;Mistry, M.;Udwadia, F. E.;Schaal, S. (2005). A new methodology for robot control design, The 5th ASME International Conference on Multibody Systems, Nonlinear Dynamics, and Control (MSNDC 2005).
[Keywords: robot control, nonlinear control, gauss principle]
[Detail] [BibTeX] [PDF]
Billard, A.;Epars, Y.;Calinon, S.;Cheng, G.;Schaal, S. (2004). Discovering optimal imitation strategies, Robotics and Autonomous Systems, 47, 2-3, pp.68-77.
[Keywords: imitation, intent extraction, motor control]
[Detail] [BibTeX]
Nakanishi, J.;Farrell, J. A.;Schaal, S. (2004). Learning Composite Adaptive Control for a Class of Nonlinear Systems, IEEE International Conference on Robotics and Automation, pp.2647-2652.
[Keywords: adaptive control, learning, composite control law, provably stable, locally weighted regression]
[Detail] [BibTeX] [PDF]
Nakanishi, J.;Schaal, S. (2004). Feedback error learning and nonlinear adaptive control, Neural Networks, 17, 10, pp.1453-1465.
[Keywords: adaptive control, feedback error learning, learning]
[Detail] [BibTeX] [PDF]
Schaal, S.;Ijspeert, A.;Billard, A. (2004). Computational approaches to motor learning by imitation, in: Frith, C. D.;Wolpert, D. (eds.), The Neuroscience of Social Interaction, 1431, pp.199-218, Oxford University Press.
[Keywords: imitation learning
computational
review
movement primitives
duality of movement generation and recognition
motor control]
[Detail] [BibTeX] [PDF]
Mohajerian, P.;Peters, J.;Ijspeert, A.;Schaal, S. (2003). A unifying computational framework for optimization and dynamic systemsapproaches to motor control, Proceedings of the 10th Joint Symposium on Neural Computation (JSNC 2003).
[Keywords: computational motor control, optimization, dynamic systems, formal modeling]
[Detail] [BibTeX] [PDF]
Schaal, S. (2003). Dynamic movement primitives - A framework for motor control in humans and humanoid robots, The International Symposium on Adaptive Motion of Animals and Machines.
[Keywords: movement primitives, behaviors, dynamic systems, computational motor control, attractor landscapes, fmri, behavioral evidence]
[Detail] [BibTeX] [PDF]
Schaal, S. (2003). Movement planning and imitation by shaping nonlinear attractors, Proceedings of the 12th Yale Workshop on Adaptive and Learning Systems.
[Keywords: movement primitives, behaviors, dynamic systems, computational motor control, attractor landscapes]
[Detail] [BibTeX] [PDF]
Schaal, S.;Ijspeert, A.;Billard, A. (2003). Computational approaches to motor learning by imitation, Philosophical Transaction of the Royal Society of London: Series B, Biological Sciences, 358, 1431, pp.537-547.
[Keywords: imitation learning, computational, review, movement primitives, duality of movement generation and recognition, motor control]
[Detail] [BibTeX] [PDF]
Ijspeert, J. A.;Nakanishi, J.;Schaal, S. (2002). Movement imitation with nonlinear dynamical systems in humanoid robots, International Conference on Robotics and Automation (ICRA2002).
[Keywords: movement primitives, behaviors, dynamic systems, computational motor control, attractor landscapes, humanoid robotics, overall best paper award]
[Detail] [BibTeX] [PDF]
Ijspeert, J. A.;Nakanishi, J.;Schaal, S. (2002). Learning rhythmic movements by demonstration using nonlinear oscillators, IEEE International Conference on Intelligent Robots and Systems (IROS 2002), pp.958-963, Piscataway, NJ: IEEE.
[Keywords: movement primitives, behaviors, dynamic systems, computational motor control, attractor landscapes, discrete, rhythmic]
[Detail] [BibTeX] [PDF]
Mehta, B.;Schaal, S. (2002). Forward models in visuomotor control, J Neurophysiol, 88, 2, pp.942-53.
[Keywords: internal models, forward models, delay compensation, blank out, pole balancing, predictive control]
[Detail] [BibTeX] [PDF]
Nakanishi, J.;Farrell, J. A.;Schaal, S. (2002). A locally weighted learning composite adaptive controller with structure adaptation, IEEE International Conference on Intelligent Robots and Systems (IROS 2002).
[Keywords: adaptive control, learning, composite control law, provably stable, locally weighted regression]
[Detail] [BibTeX] [PDF]
Vijayakumar, S.;D'Souza, A.;Shibata, T.;Conradt, J.;Schaal, S. (2002). Statistical learning for humanoid robots, Autonomous Robots, 12, 1, pp.59-72.
[Keywords: statistical learning, nonparametric regression, distance metric, dimensionality reduction, high dimensional learning, humanoid robotics, oculomotor control, internal models]
[Detail] [BibTeX] [PDF]
D'Souza, A.;Vijayakumar, S.;Schaal, S. (2001). Learning inverse kinematics, IEEE International Conference on Intelligent Robots and Systems (IROS 2001), Piscataway, NJ: IEEE.
[Keywords: inverse kinematics, humanoid robotics, high dimensional, resolved motion rate control, local learning, null space optimization]
[Detail] [BibTeX] [PDF]
Ijspeert, A.;Nakanishi, J.;Schaal, S. (2001). Trajectory formation for imitation with nonlinear dynamical systems, IEEE International Conference on Intelligent Robots and Systems (IROS 2001), pp.752-757.
[Keywords: movement primitives
behaviors
dynamic systems
computational motor control
attractor landscapes]
[Detail] [BibTeX] [PDF]
Ijspeert, A. J.;Nakanishi, J.;Shibata, T.;Schaal, S. (2001). Nonlinear dynamical systems for imitation with humanoid robots, Humanoids2001, Second IEEE-RAS International Conference on Humanoid Robots.
[Keywords: movement primitives, behaviors, dynamic systems, computational motor control, attractor landscapes, humanoid robotics]
[Detail] [BibTeX] [PDF]
Kotosaka, S.;Schaal, S. (2001). Synchronized robot drumming by neural oscillator, Journal of the Robotics Society of Japan, 19, 1, pp.116-123.
[Keywords: movement primitives, behaviors, dynamic systems, computational motor control, attractor landscapes, humanoid robotics, drumming, synchronization, best annual paper award of the japanese robotics society]
[Detail] [BibTeX]
Schaal, S.;Sternad, D. (2001). Origins and violations of the 2/3 power law in rhythmic 3D movements, Experimental Brain Research, 136, pp.60-72.
[Keywords: computational motor control, trajectory planning, power law, movement segmentation, motor primitives, rhythmic movement]
[Detail] [BibTeX] [PDF]
Shibata, T.;Schaal, S. (2001). Biomimetic gaze stabilization based on feedback-error learning with nonparametric regression networks, Neural Networks, 14, 2, pp.201-216.
[Keywords: oculomotor control, cerebellum, vor, okr, learning, locally weighted learning, feedback error learning, best annual paper award of the japanese neural networks society]
[Detail] [BibTeX] [PDF]
Shibata, T.;Schaal, S. (2001). Biomimetic smooth pursuit based on fast learning of the target dynamics, IEEE International Conference on Intelligent Robots and Systems (IROS 2001).
[Keywords: oculomotor control, cerebellum, smooth pursuit learning, locally weighted learning, tracking, target dynamics]
[Detail] [BibTeX] [PDF]
Shibata, T.;Vijayakumar, S.;Conradt, J.;Schaal, S. (2001). Biomimetic oculomotor control, Adaptive Behavior, 9, 3/4, pp.189-207.
[Keywords: oculomotor control, cerebellum, smooth pursuit learning, locally weighted learning, tracking, target dynamics, visual attention, humanoid robotics]
[Detail] [BibTeX] [PDF]
Shibata, T.;Vijayakumar, S.;Conradt, J.;Schaal, S. (2001). Humanoid oculomotor control based on concepts of computational neuroscience, Humanoids2001, Second IEEE-RAS International Conference on Humanoid Robots.
[Keywords: oculomotor control, smooth pursuit learning, locally weighted learning, tracking, target dynamics, vor, visual attention, okr]
[Detail] [BibTeX] [PDF]
Sternad, D.;Duarte, M.;Katsumata, H.;Schaal, S. (2001). Bouncing a ball: Tuning into dynamic stability, Journal of Experimental Psychology: Human Perception and Performance, 27, 5, pp.1163-1184.
[Keywords: dynamic systems approach, computational motor control, task dynamics, perception-action coupling, movement primitives]
[Detail] [BibTeX]
Vijayakumar, S.;Conradt, J.;Shibata, T.;Schaal, S. (2001). Overt visual attention for a humanoid robot, IEEE International Conference on Intelligent Robots and Systems (IROS 2001).
[Keywords: oculomotor control, visual attention, dynamic fields, saccades]
[Detail] [BibTeX] [PDF]
Atkeson, C. G.;Hale, J.;Kawato, M.;Kotosaka, S.;Pollick, F.;Riley, M.;Schaal, S.;Shibata, S.;Tevatia, G.;Ude, A. (2000). Using Humanoid Robots to Study Human Behaviour, IEEE Intelligent Systems, 15, 4, pp.46-56.
[Keywords: humanoid robotics, motor primitives, motion capture, computational motor control]
[Detail] [BibTeX] [PDF]
Kotosaka, S.;Shibata, T.;Schaal, S. (2000). Humanoid Robot DB, Proceedings of the International Conference on Machine Automation (ICMA2000), pp.21-26.
[Keywords: humanoid robotics, computational motor control]
[Detail] [BibTeX]
Nakanishi, J.;Fukuda, T.;Koditschek, D. E. (2000). A brachiating robot controller, IEEE Transactions on Robotics and Automation, 16, 2, pp.109-123.
[Keywords: dynamic systems, nonlinear robot control, brachiation, dynamically dexterous robotics, limit cycles, locomotion, swing map, symmetry, target dynamics, task encoding, underactuated system.]
[Detail] [BibTeX] [PDF]
Schaal, S.;Sternad, D.;Dean, W.;Kotoska, S.;Osu, R.;Kawato, M. (2000). Reciprocal excitation between biological and robotic research, Sensor Fusion and Decentralized Control in Robotic Systems III, Proceedings of SPIE, 4196, pp.30-40.
[Keywords: humanoid robotics, computational motor control, neuroscience, dynamic systems approach, pattern generators]
[Detail] [BibTeX] [PDF]
Shibata, T.;Schaal, S. (2000). Biomimetic gaze stabilization, in: Demiris, J.;Birk, A. (eds.), Robot learning: an Interdisciplinary approach, pp.31-52, World Scientific.
[Keywords: oculomotor control, cerebellum, vor, okr, learning, locally weighted learning, feedback error learning]
[Detail] [BibTeX] [PDF]
Shibata, T.;Schaal, S. (2000). Fast learning of biomimetic oculomotor control with nonparametric regression networks, International Conference on Robotics and Automation (ICRA2000), pp.3847-3854.
[Keywords: oculomotor control, cerebellum, vor, okr, learning, locally weighted learning, feedback error learning]
[Detail] [BibTeX] [PDF]
Sternad, D.;Dean, W. J.;Schaal, S. (2000). Interaction of rhythmic and discrete pattern generators in single joint movements, Human Movement Science, 19, 4, pp.627-665.
[Keywords: movement primitives, behaviors, dynamic systems, computational motor control, movement sequencing, discrete, rhythmic, superposition, phase resetting]
[Detail] [BibTeX] [PDF]
Sternad, D.;Duarte, M.;Katsumata, H.;Schaal, S. (2000). Dynamics of a bouncing ball in human performance, Physical Review E, 63, 011902, pp.1-8.
[Keywords: dynamic systems approach, computational motor control, task dynamics, perception-action coupling, movement primitives, special press comment]
[Detail] [BibTeX] [PDF]
Tevatia, G.;Schaal, S. (2000). Inverse kinematics for humanoid robots, International Conference on Robotics and Automation (ICRA2000), pp.294-299.
[Keywords: inverse kinematics
humanoid robotics
high dimensional
resolved motion rate control
efficient
extended jacobian]
[Detail] [BibTeX] [PDF]
Sternad, D.;Schaal, D. (1999). Segmentation of endpoint trajectories does not imply segmented control, Experimental Brain Research, 124, 1, pp.118-136.
[Keywords: trajectory formation, segementation, movement primitives, motor control, piecewise planarity]
[Detail] [BibTeX] [PDF]
Schaal, S.;Sternad, D. (1998). Programmable pattern generators, 3rd International Conference on Computational Intelligence in Neuroscience, pp.48-51.
[Keywords: movement primitives
behaviors
dynamic systems
computational motor control
attractor landscapes]
[Detail] [BibTeX] [PDF]
Shibata, T.;Schaal, S. (1998). Biomimetic gaze stabilization based on a study of the vestibulocerebellum, European Workshop on Learning Robots, pp.84-94.
[Keywords: oculomotor control, cerebellum, vor, okr, learning, locally weighted learning, feedback error learning]
[Detail] [BibTeX] [PDF]
Shibata, T.;Schaal, S. (1998). Towards biomimetic vision, International Conference on Intelligence Robots and Systems, pp.872-879.
[Keywords: oculomotor control, cerebellum, vor, okr, learning, locally weighted learning, feedback error learning]
[Detail] [BibTeX] [PDF]
Atkeson, C. G.;Moore, A. W.;Schaal, S. (1997). Locally weighted learning for control, Artificial Intelligence Review, 11, 1-5, pp.75-113.
[Keywords: statistical learning, nonparametric regression, distance metric, lazy learning, learning control, reinforcement learning]
[Detail] [BibTeX] [PDF]
Schaal, S.;Sternad, D.;Atkeson, C. G. (1996). One-handed juggling: A dynamical approach to a rhythmic movement task, Journal of Motor Behavior, 28, 2, pp.165-183.
[Keywords: biological motor control, trajectory formation, nonlinear dynamics, task dynamics, movement primitives]
[Detail] [BibTeX] [PDF]
Sternad, D.;Schaal, S.;Atkeson, C. G. (1995). Batting a ball: Dynamics of a rhythmic skill, in: Bardy, B.;Bostma, R.;Guiard, Y. (eds.), Studies in Perception and Action, pp.119-122, Erlbaum.
[Keywords: motor control, nonlinear dynamics, task dynamics, ball bouncing, open-loop stability]
[Detail] [BibTeX]