Abstract: Manipulation without grasping, known as non-prehensile manipulation, is essential for dexterous robots in contact-rich environments, but presents many challenges relating with under-actuation, hybrid-dynamics, and frictional uncertainty. Additionally, object occlusions in a scenario of contact uncertainty, and where there is a relative motion between the object and the robot, becomes a critical problem. In this talk, I will present some of our recent work on non-prehensile manipulation through a model-free paradigm with reinforcement learning. In particular, I will discuss our findings on leveraging multimodal categorical exploration to unlock significant performance improvements, as well as learning visuotactile state estimators and uncertainty-aware control policies to enable non-prehensile manipulation under visual occlusions.
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Meeting ID: 337 758 277 805
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