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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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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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