A prerequisite for achieving brain-like intelligence is the ability to rapidly learn new behaviors and actions. A fundamental mechanism for rapid learning in humans is imitation: children routinely learn new skills (e.g., opening a door or tying a shoe lace) by imitating their parents; adults continue to learn by imitating skilled instructors (e.g., in tennis). In this chapter, we propose a probabilistic framework for imitation learning in robots that is inspired by how humans learn from imitation and exploration. Rather than relying on complex (and often brittle) physics- based models, the robot learns a dynamic Bayesian network that captures its dynamics directly in terms of sensor measurements and actions during an imitation-guided exploration phase. After learning, actions are selected based on probabilistic inference in the learned Bayesian network. We present results demonstrating that a 25-degree-of-freedom humanoid robot can learn dynamically stable, full-body imitative motions simply by observing a human demonstrator. © Springer-Verlag Berlin Heidelberg 2009.
CITATION STYLE
Grimes, D. B., & Rao, R. P. N. (2009). Learning actions through imitation and exploration: Towards humanoid robots that learn from humans. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5436, pp. 103–138). https://doi.org/10.1007/978-3-642-00616-6_7
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