用高保真渲染与力反馈，少样本实现仿真到现实的机器人操作迁移。
Few-shot Sim2Real Based on High Fidelity Rendering with Force Feedback Teleoperation
- 引入力反馈设备提升仿真中抓取精度，增强操作真实感。
- 高保真视觉渲染使任务成功率提升37%，减少90%真实数据依赖。
- 适合需要少量实物数据的机器人控制研究者使用。
遥操作为机器人数据采集和人机交互提供了有前景的方案。然而，现有数据采集方法在时间和空间效率上仍受限，且基于仿真的数据采集流程尚不清晰。核心挑战在于如何在降低对真实世界数据依赖的同时提升任务表现。为此，我们提出一种融合力反馈的遥操作管道，用于在仿真中采集机器人操作数据，并训练少样本的仿真到现实（sim2real）视觉-运动策略。通过集成力反馈设备，系统可提供精确的末端执行器抓握力反馈。在多种操作任务上的实验表明，力反馈显著提升了成功率与执行效率，尤其在仿真环境中效果突出。此外，不同视觉渲染质量的对比实验显示，提升仿真中的视觉真实性可大幅提高任务表现，并减少对真实数据的需求。
原文摘要 · Abstract (English)
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.
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