The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Task-Specific Generalization of Discrete and Periodic Dynamic Movement Primitives
Abstract
Acquisition of new sensorimotor knowledge by imitation is a promising paradigm for robot learning. To be effective, action learning should not be limited to direct replication of movements obtained during training but must also enable the generation of actions in situations a robot has never encountered before. This paper describes a methodology that enables the generalization of the available sensorimotor knowledge. New actions are synthesized by the application of statistical methods, where the goal and other characteristics of an action are utilized as queries to create a suitable control policy, taking into account the current state of the world. Nonlinear dynamic systems are employed as a motor representation. The proposed approach enables the generation of a wide range of policies without requiring an expert to modify the underlying representations to account for different task-specific features and perceptual feedback. The paper also demonstrates that the proposed methodology can be integrated with an active vision system of a humanoid robot. 3-D vision data are used to provide query points for statistical generalization. While 3-D vision on humanoid robots with complex oculomotor systems is often difficult due to the modeling uncertainties, we show that these uncertainties can be accounted for by the proposed approach.
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Cites
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Cited by
(2013) · Lincoln Repository (University of Lincoln) · Cited by 503
A tutorial on task-parameterized movement learning and retrieval
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Dynamic movement primitives in robotics: A tutorial survey
Saveriano, M., Abu-Dakka, F. J., Kramberger, A., & Peternel, L. (2023) · The International Journal of Robotics Research · doi:10.1177/02783649231201196 · Cited by 267
A Survey on Policy Search Algorithms for Learning Robot Controllers in a Handful of Trials
Chatzilygeroudis, K., Vassiliades, V., Stulp, F., Calinon, S., & Mouret, J.-B. (2020) · IEEE Transactions on Robotics · doi:10.1109/tro.2019.2958211 · Cited by 160
Trajectory planning for optimal robot catching in real-time
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Metadata
- Authors
- Ude, A., Gams, A., Asfour, T., & Morimoto, J.
- Venue
- IEEE Transactions on Robotics
- Published
- 2010-10
- Volume
- 26
- Issue
- 5
- Pages
- 800-815
- ISSN
- 1552-3098 (ISSN-L)
- 1941-0468
- OpenAlex
- W2116226448
- Cited by
- 355
- References
- 50
- FWCI
- 29.74
- Type
- journal article
- Language
- en
- Open access
- closed
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