The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Robot learning from demonstration by constructing skill trees
Abstract
We describe CST, an online algorithm for constructing skill trees from demonstration trajectories. CST segments a demonstration trajectory into a chain of component skills, where each skill has a goal and is assigned a suitable abstraction from an abstraction library. These properties permit skills to be improved efficiently using a policy learning algorithm. Chains from multiple demonstration trajectories are merged into a skill tree. We show that CST can be used to acquire skills from human demonstration in a dynamic continuous domain, and from both expert demonstration and learned control sequences on the uBot-5 mobile manipulator.
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Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Self-citationA survey of robot learning from demonstration
Argall, B. D., Chernova, S., Veloso, M., & Browning, B. (2009) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2008.10.024 · Cited by 3,227
Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
Sutton, R. S., Precup, D., & Singh, S. (1999) · Artificial Intelligence · doi:10.1016/s0004-3702(99)00052-1 · Cited by 3,119
Cited by
Reinforcement learning in robotics: A survey
Kober, J., Bagnell, J. A., & Peters, J. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913495721 · Cited by 3,160
How to train your robot with deep reinforcement learning: Lessons we have learned
Ibarz, J., Tan, J., Finn, C., Kalakrishnan, M., Pastor, P., & Levine, S. (2021) · The International Journal of Robotics Research · doi:10.1177/0278364920987859 · Cited by 570
(2013) · Lincoln Repository (University of Lincoln) · Cited by 503
An algorithmic perspective on imitation learning
Osa, T., Pajarinen, J., Neumann, G., Bagnell, J. A., Abbeel, P., & Peters, J. (2018) · Foundations and Trends in Robotics · doi:10.1561/2300000053 · Cited by 405
Modular Multitask Reinforcement Learning with Policy Sketches
Andreas, J., Klein, D., & Levine, S. (2016) · arXiv · doi:10.48550/arxiv.1611.01796 · Cited by 274
Metadata
- Authors
- Konidaris, G., Kuindersma, S., Grupen, R., & Barto, A.
- Venue
- The International Journal of Robotics Research
- Publisher
- SAGE Publications
- Published
- 2011-12-05
- Volume
- 31
- Issue
- 3
- Pages
- 360-375
- ISSN
- 0278-3649 (ISSN-L)
- 1741-3176
- OpenAlex
- W1963873191
- Cited by
- 286
- References
- 3
- FWCI
- 13.01
- Type
- journal article
- Language
- en
- Open access
- closed
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- Konidaris, G., Kuindersma, S., Grupen, R., & Barto, A. (2012). Robot learning from demonstration by constructing skill trees. The International Journal of Robotics Research. https://doi.org/10.1177/0278364911428653
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