From Learning Movement Primitives to Associative Skill Memories
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Skillful and goal-directed interaction with a dynamically changing world is among the hallmarks of human perception and motor control. Understanding the mechanisms of such skills and how they are learned is a long-standing question in both neuroscience and technology. This talk develops a general framework of how motor skills can be learned. At the heart of our work is a general representation of motor skills in terms of movement primitives as nonlinear attractor systems, the ability to generalize a motor skill to novel situations and to adjust it to sudden perturbations, and the ability to employ imitation learning, trial-and-error learning, and model-based learning to improve planning and control of a motor skill. Our framework has close connections to known phenomena in behavioral and neurosciences, and it also intuitively bridges between dynamic systems theory and optimization theory in motor control, two rather disjoint approaches. We evaluate our approach in various behavioral and robotic studies with anthropomorphic and humanoid robots. Finally, we discuss how to go beyond simple movement primitives to a more complete perception-action-learning system, and speculate on the concept of Associative Skill Memories as an interesting approach.
- IRIM Seminar Series