外观
论文arXiv 2506.151462025-06-18
TACT算法融合触觉模态实现人形机器人全身接触操作与行走
本文由 AI 根据公开内容整理，可能有误，请以原文为准：TACT: Humanoid Whole-body Contact Manipulation through Deep Imitation Learning with Tactile Modality（arXiv 2506.15146）
核心内容
针对人形机器人全身接触操作中运动生成计算成本高及广域接触测量难的问题，研究团队开发了基于人类遥操作数据的深度模仿学习控制系统TACT。该系统专为上半身配备触觉传感器的人形机器人设计，旨在通过多模态感知提升非结构化环境下的操作能力，并在真机实验中验证了其在动态平衡与复杂接触任务中的有效性。
TACT策略以关节位置、视觉图像及触觉信号为联合输入，结合重定向与双足运动控制模型，驱动全尺寸人形机器人RHP7 Kaleido实现边走边操作。实验证实，相比单一模态，视觉与触觉的协同输入显著增强了机器人在涉及大面积及精细接触场景下的操作鲁棒性，有效解决了纯视觉方案在接触状态估计上的盲区问题。
关键看点
- 多模态融合：TACT策略联合处理关节位置、视觉与触觉三类信号，弥补单一视觉在广域接触感知中的不足
- 全身协调控制：集成动作重定向与双足步态控制器，支持RHP7 Kaleido在维持动态平衡行走时执行上身接触操作
- 数据驱动范式：基于人类遥操作演示数据进行深度模仿学习，降低全身高自由度接触运动的解析建模难度
- 鲁棒性验证：对比实验证明视触融合输入显著提升机器人在大面积及精细接触任务中的操作成功率与稳定性
工程与产业观察
TACT证明了触觉反馈在人形机器人全身操作中的不可替代性，尤其在动态行走与非结构化接触场景中。该工作为具身智能提供了从纯视觉向视触融合演进的技术路径，提示硬件研发需重视上身触觉阵列部署，算法端应强化多模态对齐与接触状态估计能力。
英文摘要 (Abstract)
Manipulation with whole-body contact by humanoid robots offers distinct advantages, including enhanced stability and reduced load. On the other hand, we need to address challenges such as the increased computational cost of motion generation and the difficulty of measuring broad-area contact. We therefore have developed a humanoid control system that allows a humanoid robot equipped with tactile sensors on its upper body to learn a policy for whole-body manipulation through imitation learning based on human teleoperation data. This policy, named tactile-modality extended ACT (TACT), has a feature to take multiple sensor modalities as input, including joint position, vision, and tactile measurements. Furthermore, by integrating this policy with retargeting and locomotion control based on a biped model, we demonstrate that the life-size humanoid robot RHP7 Kaleido is capable of achieving whole-body contact manipulation while maintaining balance and walking. Through detailed experimental verification, we show that inputting both vision and tactile modalities into the policy contributes to improving the robustness of manipulation involving broad and delicate contact.
作者：Masaki Murooka, Takahiro Hoshi, Kensuke Fukumitsu, Shimpei Masuda, Marwan Hamze, Tomoya Sasaki, Mitsuharu Morisawa, Eiichi Yoshida
来源
本页事实对应的公开页面，以原文为准。