2021/01/07 by Peter Zachares, Michelle A. Lee, Zachares, Peter A. +5
Computer Science · Engineering · Neuroscience · #FOS: Computer and information sciences #Reinforcement Learning in Robotics #Robot Manipulation and Learning #Robotics (cs.RO) #Tactile and Sensory Interactions
paper · pdf · doi:10.48550/arxiv.2101.02725
openalex publication_date 2021/01/07 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
A key challenge towards the goal of multi-part assembly tasks is finding\nrobust sensorimotor control methods in the presence of uncertainty. In contrast\nto previous works that rely on a priori knowledge on whether two parts match,\nwe aim to learn this through physical interaction. We propose a hierarchical\napproach that enables a robot to autonomously assemble parts while being\nuncertain about part types and positions. In particular, our probabilistic\napproach learns a set of differentiable filters that leverage the tactile\nsensorimotor trace from failed assembly attempts to update its belief about\npart position and type. This enables a robot to overcome assembly failure. We\ndemonstrate the effectiveness of our approach on a set of object fitting tasks.\nThe experimental results indicate that our proposed approach achieves higher\nprecision in object position and type estimation, and accomplishes object\nfitting tasks faster than baselines.\n