未知の穴への実世界ペグ挿入のためのシミュレーション学習に基づく視覚ポリシー
Learning A Simulation-based Visual Policy for Real-world Peg In Unseen Holes
シミュレーションで汎用ポリシーを学習し、実世界の未知形状の穴へ最小限のsim2realコストで適応するペグ挿入手法を提案。1分間の教示で自動収集したデータでセグメンテーションを微調整し、EV充電システムで10/10の成功率を達成。
著者: Liang Xie, Hongxiang Yu, Kechun Xu, Tong Yang, Minhang Wang, Haojian Lu, Rong Xiong, Yue Wang
分類: cs.RO, cs.AI
原文アブストラクト
This paper proposes a learning-based visual peg-in-hole that enables training with several shapes in simulation, and adapting to arbitrary unseen shapes in real world with minimal sim-to-real cost. The core idea is to decouple the generalization of the sensory-motor policy to the design of a fast-adaptable perception module and a simulated generic policy module. The framework consists of a segmentation network (SN), a virtual sensor network (VSN), and a controller network (CN). Concretely, the VSN is trained to measure the pose of the unseen shape from a segmented image. After that, given the shape-agnostic pose measurement, the CN is trained to achieve generic peg-in-hole. Finally, when applying to real unseen holes, we only have to fine-tune the SN required by the simulated VSN+CN. To further minimize the transfer cost, we propose to automatically collect and annotate the data for the SN after one-minute human teaching. Simulated and real-world results are presented under the configurations of eye-to/in-hand. An electric vehicle charging system with the proposed policy inside achieves a 10/10 success rate in 2-3s, using only hundreds of auto-labeled samples for the SN transfer.
