力覚フィードバック遠隔操作と高忠実度レンダリングによる少数ショットSim2Real
Few-shot Sim2Real Based on High Fidelity Rendering with Force Feedback Teleoperation
力覚フィードバック付き遠隔操作でシミュレーション上の操作データを収集し、少数の実機データで視覚運動ポリシーを学習するSim2Real手法を提案。高忠実度レンダリングが実データ依存を減らしつつタスク性能を向上させることを示した。
著者: Yanwen Zou, Junda Huang, Boyuan Liang, Honghao Guo, Zhengyang Liu, Xin Ma, Jianshu Zhou, Masayoshi Tomizuka
分類: cs.RO
原文アブストラクト
Teleoperation offers a promising approach to robotic data collection and human-robot interaction. However, existing teleoperation methods for data collection are still limited by efficiency constraints in time and space, and the pipeline for simulation-based data collection remains unclear. The problem is how to enhance task performance while minimizing reliance on real-world data. To address this challenge, we propose a teleoperation pipeline for collecting robotic manipulation data in simulation and training a few-shot sim-to-real visual-motor policy. Force feedback devices are integrated into the teleoperation system to provide precise end-effector gripping force feedback. Experiments across various manipulation tasks demonstrate that force feedback significantly improves both success rates and execution efficiency, particularly in simulation. Furthermore, experiments with different levels of visual rendering quality reveal that enhanced visual realism in simulation substantially boosts task performance while reducing the need for real-world data.
