Handling of multiple constraints and motion alternatives in a robot programming by demonstration framework
Open accessAbstract
We consider the problem of learning robust models of robot motion through demonstration. An approach based on Hidden Markov Model (HMM) and Gaussian Mixture Regression (GMR) is proposed to extract redundancies across multiple demonstrations, and build a time-independent model of a set of movements demonstrated by a human user. Two experiments are presented to validate the method, that consist of learning to hit a ball with a robotic arm, and of teaching a humanoid robot to manipulate a spoon to feed another humanoid. The experiments demonstrate that the proposed model can efficiently handle several aspects of learning by imitation. We first show that it can be utilized in an unsupervised learning manner, where the robot is autonomously organizing and encoding variants of motion from the multiple demonstrations. We then show that the approach allows to robustly generalize the observed skill by taking into account multiple constraints in task space during reproduction.
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The approach of Calinon and Billard assumes access to a featurization of the environment, since regression requires an input vector of fixed dimensionality.
“The approach of Calinon and Billard assumes access to a featurization of the environment, since regression requires an input vector of fixed dimensionality.”
From Learning from Demonstrations Through the Use of Non-rigid Registration (2016) · cites this paper as [9]
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- Authors
- Calinon, S., D'halluin, F., Caldwell, D. G., & Billard, A. G.
- Affiliations
- Italian Institute of Technology (IT)
- Venue
- 2009 9th IEEE-RAS International Conference on Humanoid Robots
- Publisher
- IEEE
- Published
- 2009-12
- Pages
- 582-588
- OpenAlex
- W2131908539
- Cited by
- 73
- References
- 22
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 2 · 2021: 3 · 2020: 1 · 2019: 2 · 2018: 5 · 2017: 4 · 2016: 7 · 2015: 8 · 2014: 5 · 2013: 6 · 2012: 6
- FWCI
- 14.50
- Type
- conference paper
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Locations
- IEEE-RAS International Conference on Humanoid Robots (Humanoids) (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- 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
- robot programming by demonstration, imitation learning, hidden Markov models, Gaussian mixture regression, motion generalization, humanoid robots, unsupervised learning
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