为低成本机械臂遥操作增加力反馈，提升操控真实感与任务成功率
Improving Low-Cost Teleoperation: Augmenting GELLO with Force
- 在原有位置控制基础上加入力反馈，实现触觉感知
- 将力信息融入模仿学习数据采集与训练，提升模型性能
- 实测显示力信息显著改善复杂操作任务的成功率
本文扩展了低成本的GELLO遥操作系统，原系统仅支持关节位置控制。新增两项改进：一是实现力反馈，使用户在交互时能感知环境阻力；二是将力信息引入模仿学习的数据收集与模型训练过程。我们在配备Franka Panda机器人的GELLO系统上进行验证，通过用户研究对比了含与不含力信息的策略在多种模拟及真实精细操作任务中的表现。结果显示，有机器人经验的用户更偏好新控制器，且多数任务中加入力输入后任务成功率显著提升。
原文摘要 · Abstract (English)
In this work we extend the low-cost GELLO teleoperation system, initially designed for joint position control, with additional force information. Our first extension is to implement force feedback, allowing users to feel resistance when interacting with the environment. Our second extension is to add force information into the data collection process and training of imitation learning models. We validate our additions by implementing these on a GELLO system with a Franka Panda arm as the follower robot, performing a user study, and comparing the performance of policies trained with and without force information on a range of simulated and real dexterous manipulation tasks. Qualitatively, users with robotics experience preferred our controller, and the addition of force inputs improved task success on the majority of tasks.
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