HACo：学习力觉感知灵巧操作中的触觉主动柔顺
HACo: Learning Haptic Active Compliance for Force-Aware Dexterous Manipulation
- The University of Hong Kong（香港大学）
- Beijing Academy of Artificial Intelligence (BAAI)（北京人工智能研究院）
- Johns Hopkins University（约翰霍普金斯大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
HACo通过触觉主动柔顺策略，利用指尖触觉和关节力矩反馈直接学习力调节动作，在真实基准上以83%成功率远超基线，实现无需显式接触建模的闭环力调节。
AI中文摘要：
接触丰富的灵巧操作需要策略将物理反馈转化为运动指令，同时在不断演化的多接触交互中调节交互载荷。这需要接触状态的触觉观测以及显示指令应如何调整的动作监督。现有策略往往忽视互补的指尖触觉和关节力矩反馈，而常见的动作目标要么编码过大的载荷，要么省略受物体约束的运动。我们提出HACo，一种触觉主动柔顺策略，直接从触觉反馈中学习力调节动作。柔顺调节遥操作将操作员输入转换为控制器可执行的柔顺动作，在调节载荷的同时保留运动意图。HACo直接学习这些动作，利用指令-状态差异作为辅助的柔顺意图监督。它结合局部指尖触觉响应与关节力矩反馈，捕捉通过铰接手传递的载荷，包括超出触觉覆盖范围的接触。柔顺接地模块使用门控触觉交叉注意力，将动作生成基于不断演化的触觉状态，实现无需显式在线接触建模的闭环力调节。我们在真实世界基准上评估HACo，涵盖多接触摩擦、切向交互、脆弱曲面接触、旋转力矩和可变形物体操作。每个任务20次试验中，HACo平均成功率达83%，而最强评估基线为35%。这些结果展示了在多样化的力敏感灵巧操作任务中的主动柔顺能力。
英文摘要：
Contact-rich dexterous manipulation requires policies that translate physical feedback into motion commands while regulating interaction loads across evolving multi-contact interactions. This requires haptic observations of contact state and action supervision showing how commands should adapt. Existing policies often overlook complementary fingertip tactile and joint-torque feedback, while common action targets either encode excessive loading or omit motion constrained by the object. We introduce HACo, a Haptic Active Compliance policy that learns force-regulating actions directly from haptic feedback. Compliance-regulated teleoperation converts operator inputs into controller-executable compliant actions that preserve motion intent while regulating loads. HACo learns these actions directly, using command-state discrepancy as auxiliary compliant-intent supervision. It combines local fingertip tactile responses with joint-torque feedback capturing load transmission through the articulated hand, including contacts beyond tactile coverage. A Compliance Grounding Module uses gated haptic cross-attention to ground action generation in the evolving haptic state, enabling closed-loop force regulation without explicit online contact modeling. We evaluate HACo on a real-world benchmark covering multi-contact friction, tangential interaction, fragile curved-surface contact, rotational torque, and deformable-object manipulation. Across 20 trials per task, HACo achieves an 83% mean success rate, compared with 35% for the strongest evaluated baseline. These results demonstrate active compliance across diverse force-sensitive dexterous manipulation tasks.
