论文/arXiv 2609.36596·29 天前

HACo：学习用于力感知灵巧操作的触觉主动顺应

HACo通过触觉与关节力矩反馈学习主动顺应动作，并在真实多接触灵巧操作基准上验证力调节能力。

AI 整理

• 硬件零部件
• 本体 SDK 与控制
• 数据与采集

原文链接：HACo: Learning Haptic Active Compliance for Force-Aware Dexterous Manipulation（arXiv 2609.36596 · 发布日期：2026-09-29）

HACo: Learning Haptic Active Compliance for Force-Aware Dexterous Manipulation

HACo通过触觉与关节力矩反馈学习主动顺应动作，并在真实多接触灵巧操作基准上验证力调节能力。

核心要点

• 多接触灵巧操作需将物理反馈转为运动命令，并在接触演化中调节载荷。
• 方法融合指尖触觉与关节力矩，用顺应遥操作动作和命令-状态差监督学习。
• Compliance Grounding Module用门控触觉交叉注意力，将动作生成锚定到演化触觉状态。
• 真实基准每任务20次试验，HACo平均成功率83%，最强评估基线为35%。

验证范围与局限：验证范围为真实世界基准中的多接触摩擦、切向交互、易碎曲面接触、旋转力矩和可变形物体操作任务。

英文摘要 (Abstract)

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.

作者：N. Ye, Yinze Zhou, Junkai Zhao, Yuhang Lu, Checheng Yu, Zhenjie Yang, Pengwei Wang, Hongyang Li

来源

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1. arXiv 2609.36596：HACo: Learning Haptic Active Compliance for Force-Aware Dexterous Manipulation访问 2026-10-21