DexForce：从动觉示教中提取力信息动作用于灵巧操作
DexForce: Extracting Force-informed Actions from Kinesthetic Demonstrations for Dexterous Manipulation
AI总结：
针对接触丰富灵巧操作中示教动作缺乏力信息的问题，提出DexForce方法，利用动觉示教中的接触力计算力信息动作，使策略平均成功率达76%，远超基线。
摘要：
模仿学习需要由状态-动作对序列组成的高质量示教。对于需要灵巧性的接触丰富型灵巧操作任务，这些状态-动作对中的动作必须产生正确的力。当前广泛使用的灵巧操作示教收集方法，由于不直观的人到机器人运动重定向以及缺乏直接触觉反馈，难以用于演示接触丰富型任务。受这些问题的驱动，我们提出了DexForce。DexForce利用在动觉示教过程中测量的接触力，来计算用于策略学习的力信息动作。我们收集了六个任务的示教，并表明基于我们的力信息动作训练的策略在所有任务上的平均成功率为76%。相比之下，直接基于不考虑接触力的动作训练的策略成功率接近零。我们还进行了一项消融研究，去除策略观测中的力数据。我们发现，虽然使用力数据从不损害策略性能，但对于需要高精度和协调性的任务（如打开AirPods充电盒和拧松螺母）帮助最大。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
