Learning Visuotactile Estimation and Control for Non-prehensile Manipulation under Occlusions

• URL: http://arxiv.org/abs/2412.13157v1
• Date: Tue, 17 Dec 2024 18:33:05 GMT
• Title: Learning Visuotactile Estimation and Control for Non-prehensile Manipulation under Occlusions
• Authors: Juan Del Aguila Ferrandis, João Moura, Sethu Vijayakumar,
• Abstract summary: Non-prehensile manipulation is essential for dexterous robots in contact-rich environments. We present a method for learning visuotactile state estimators and uncertainty-aware control policies for non-prehensile manipulation under occlusions.
• Score: 10.866026182745397
• License:
• Abstract: Manipulation without grasping, known as non-prehensile manipulation, is essential for dexterous robots in contact-rich environments, but presents many challenges relating with underactuation, hybrid-dynamics, and frictional uncertainty. Additionally, object occlusions in a scenario of contact uncertainty and where the motion of the object evolves independently from the robot becomes a critical problem, which previous literature fails to address. We present a method for learning visuotactile state estimators and uncertainty-aware control policies for non-prehensile manipulation under occlusions, by leveraging diverse interaction data from privileged policies trained in simulation. We formulate the estimator within a Bayesian deep learning framework, to model its uncertainty, and then train uncertainty-aware control policies by incorporating the pre-learned estimator into the reinforcement learning (RL) loop, both of which lead to significantly improved estimator and policy performance. Therefore, unlike prior non-prehensile research that relies on complex external perception set-ups, our method successfully handles occlusions after sim-to-real transfer to robotic hardware with a simple onboard camera. See our video: https://youtu.be/hW-C8i_HWgs.

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