The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Learning and generalization of motor skills by learning from demonstration
Abstract
We provide a general approach for learning robotic motor skills from human demonstration. To represent an observed movement, a non-linear differential equation is learned such that it reproduces this movement. Based on this representation, we build a library of movements by labeling each recorded movement according to task and context (e.g., grasping, placing, and releasing). Our differential equation is formulated such that generalization can be achieved simply by adapting a start and a goal parameter in the equation to the desired position values of a movement. For object manipulation, we present how our framework extends to the control of gripper orientation and finger position. The feasibility of our approach is demonstrated in simulation as well as on the Sarcos dextrous robot arm. The robot learned a pick-and-place operation and a water-serving task and could generalize these tasks to novel situations.
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Is imitation learning the route to humanoid robots?
Schaal, S. (1999) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(99)01327-3 · Cited by 1,363
Movement imitation with nonlinear dynamical systems in humanoid robots
Ijspeert, A. J., Nakanishi, J., & Stefan Schaal, A. (2003) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2002.1014739 · Cited by 862
Self-citationRobot learning from demonstration
Billard, A., & Siegwart, R. (2004) · Robotics Auton. Syst. · doi:10.1016/j.robot.2004.03.001 · Cited by 833
Computational approaches to motor learning by imitation
Schaal, S., Ijspeert, A. J., & Billard, A. (2003) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2002.1258 · Cited by 593
Self-citationImitation in Animals and Artifacts
Dautenhahn, K., & Nehaniv, C. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/3676.001.0001 · Cited by 586
Learning Attractor Landscapes for Learning Motor Primitives
Ijspeert, A. J., Nakanishi, J., Stefan Schaal, A., Becker, S., Thrun, S., & Obermayer, K. (2002) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 572
Behavioral dynamics of steering, obstable avoidance, and route selection.
Fajen, B. R., & Warren, W. H. (2003) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/0096-1523.29.2.343 · Cited by 453
Operational Space Control: A Theoretical and Empirical Comparison
Nakanishi, J., Cory, R., Mistry, M., Peters, J., & Stefan Schaal, A. (2008) · The International Journal of Robotics Research · doi:10.1177/0278364908091463 · Cited by 381
Hoffmann, H., Pástor, P., Park, D., & Schaal, S. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2009.5152423 · Cited by 290
Self-citationClosed-loop manipulator control using quaternion feedback
Yuan, J. S. (1988) · IEEE Journal on Robotics and Automation · doi:10.1109/56.809 · Cited by 269
Movement reproduction and obstacle avoidance with dynamic movement primitives and potential fields
Park, D., Hoffmann, H., Pastor, P., & Stefan Schaal, A. (2008) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/ichr.2008.4755937 · Cited by 267
Self-citationControl, Planning, Learning, and Imitation with Dynamic Movement Primitives
Schaal, S., Peters, J., Nakanishi, J., & Ijspeert, A. J. (2003) · MPG.PuRe (Max Planck Society) · Cited by 78
Self-citationObject Action Complexes as an Interface for Planning and Robot Control
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Perceiving Objects and Movements to Generate Actions on a Humanoid Robot
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Self-citationNakanishi, J., Mistry, M., Peters, J., & Stefan Schaal, A. (2007) · Cited by 5
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Research works printed but not linked: 1
- demiris, 1996, Imitative Learning Mechanisms in Robots and Humans, Proc Eur Workshop on Learning Robotics
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End-to-End Training of Deep Visuomotor PoliciesPreprint
Levine, S., Finn, C., Darrell, T., & Abbeel, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.00702 · Cited by 2,641
Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors
Ijspeert, A. J., Nakanishi, J., Hoffmann, H., Pástor, P., & Schaal, S. (2012) · Neural Computation · doi:10.1162/neco_a_00393 · Cited by 1,637
Self-citationDeep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Gu, S., Holly, E., Lillicrap, T., & Levine, S. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989385 · Cited by 1,582
Learning Stable Nonlinear Dynamical Systems With Gaussian Mixture Models
Khansari-Zadeh, S. M., & Billard, A. (2011) · IEEE Transactions on Robotics · doi:10.1109/tro.2011.2159412 · Cited by 771
Recent Advances in Robot Learning from Demonstration
Ravichandar, H., Polydoros, A. S., Chernova, S., & Billard, A. (2020) · Annu. Rev. Control. Robotics Auton. Syst. · doi:10.1146/annurev-control-100819-063206 · Cited by 947
Metadata
- Authors
- Pastor, P., Hoffmann, H., Asfour, T., & Schaal, S.
- Affiliations
- University of Southern California (US)
- Karlsruhe Institute of Technology (DE)
- Venue
- 2009 IEEE International Conference on Robotics and Automation
- Publisher
- IEEE
- Published
- 2009-05
- Pages
- 763-768
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2161395589
- Cited by
- 702
- References
- 15
- Citations by year
- 2026: 12 · 2025: 31 · 2024: 37 · 2023: 41 · 2022: 37 · 2021: 59 · 2020: 52 · 2019: 71 · 2018: 67 · 2017: 55 · 2016: 41 · 2015: 46 · 2014: 38 · 2013: 28 · 2012: 27
- FWCI
- 48.44
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Keywords
- imitation learning, object manipulation, pick-and-place
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