用操作中的受力数据提升灵巧操作的模仿学习效果
DexForce: Extracting Force-informed Actions from Kinesthetic Demonstrations for Dexterous Manipulation
- 通过动作中的接触力反推合理动作，改善示范质量
- 六项任务平均成功率76%，无受力信息则几乎失败
- 对高精度操作（如开AirPods盒）帮助显著
模仿学习需要高质量的状态-动作序列示范。对于依赖接触力的灵巧操作任务，动作必须产生正确的力。现有示范方法因人机运动映射不直观且缺乏触觉反馈，难以用于此类任务。为此我们提出DexForce，利用动力学示范中测得的接触力，计算出带有受力信息的动作供策略学习。我们在六个任务上收集示范，发现基于受力信息动作训练的策略在所有任务上平均成功率达76%；而直接使用未考虑接触力的动作训练的策略成功率接近零。我们还进行了消融实验，发现虽然引入力信息从不降低性能，但在需要高精度与协调的任务（如打开AirPods盒、拧螺丝）中提升最明显。
原文摘要 · Abstract (English)
Imitation learning requires high-quality demonstrations consisting of sequences of state-action pairs. For contact-rich dexterous manipulation tasks that require dexterity, the actions in these state-action pairs must produce the right forces. Current widely-used methods for collecting dexterous manipulation demonstrations are difficult to use for demonstrating contact-rich tasks due to unintuitive human-to-robot motion retargeting and the lack of direct haptic feedback. Motivated by these concerns, we propose DexForce. DexForce leverages contact forces, measured during kinesthetic demonstrations, to compute force-informed actions for policy learning. We collect demonstrations for six tasks and show that policies trained on our force-informed actions achieve an average success rate of 76% across all tasks. In contrast, policies trained directly on actions that do not account for contact forces have near-zero success rates. We also conduct a study ablating the inclusion of force data in policy observations. We find that while using force data never hurts policy performance, it helps most for tasks that require advanced levels of precision and coordination, like opening an AirPods case and unscrewing a nut.
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