Abstract

Despite significant advances in AI, robots still struggle with tasks involving physical interaction. Robots can easily beat humans at board games such as Chess or Go but are incapable of skillfully moving the game pieces by themselves (the part of the task that humans subconsciously succeed in). What makes research in robotics both hard and fascinating is that movement skills are tightly connected to our physical world and to embodied forms of intelligence. I will present an overview of the research in our group to help robots acquire manipulation skills by imitation and self-refinement. We advocate frugal learning in our research, where frugality has two goals: 1) learning manipulation skills from only few demonstrations or exploration trials; and 2) learning only the components of the skill that really need to be learned! Toward this goal, I will emphasize the roles of geometric manifolds, manipulability ellipsoids, implicit shape representations and distance fields as inductive biases to facilitate manipulation skill acquisition. For the generation of trajectories and feedback controllers, I will discuss how the underlying cost functions should take into account variations, coordination and task prioritization, where various forms of movement primitives based on Fourier or Bernstein functions can contribute to the optimization process. I will also show how ergodic control can provide a mathematical framework to generate exploration and coverage movement behaviors, which we exploit in robot drawing applications and as a way to cope with uncertainty in sensing, proprioception and motor control.

Bibtex reference

@inproceedings{Calinon25IGS,
	author={Calinon, S.},
	title={Movement Generation and Drawing in Robotics},
	booktitle={22nd Conference of the International Graphonomics Society ({IGS})},
	year={2025}
}

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