# HACo：学习用于力感知灵巧操作的触觉主动顺应

> 原文链接：https://robotics.hellobike.com/papers/2026-09-29-2609-36596
> 频道：papers | 更新日期：2026-09-29 | 标签：arxiv, cs.RO
> 核心摘要：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。

# HACo：学习用于力感知灵巧操作的触觉主动顺应

::: v-pre
> 原文链接：[HACo: Learning Haptic Active Compliance for Force-Aware Dexterous Manipulation](https://arxiv.org/abs/2609.36596)（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
:::

## 原始参考来源 (Sources)

- [arXiv 2609.36596：HACo: Learning Haptic Active Compliance for Force-Aware Dexterous Manipulation](https://arxiv.org/abs/2609.36596)