论文/arXiv 2609.35450·1 个月前

Uni-VLaT：用于人形机器人移动操作的 VLA 策略全身触觉适应

针对接触丰富的人形移动操作中视觉和本体感知不足，Uni-VLaT以全身触觉预测扩展VLA，并在五项真机任务验证。

AI 整理

• 硬件零部件
• 本体 SDK 与控制
• 数据与采集

原文链接：Uni-VLaT: Whole-Body Tactile Adaptation of VLA Policies for Humanoid Loco-Manipulation（arXiv 2609.35450 · 发布日期：2026-09-28）

Uni-VLaT: Whole-Body Tactile Adaptation of VLA Policies for Humanoid Loco-Manipulation

针对接触丰富的人形移动操作中视觉和本体感知不足，Uni-VLaT以全身触觉预测扩展VLA，并在五项真机任务验证。

核心要点

• 物理接触常决定人形机器人响应，遮挡接触区域时视觉与本体感知不足。
• Uni-VLaT加入触觉通路，并预测未来触觉、本体和视觉表征来构建多模态上下文。
• 五项真机任务中平均成功率75%，较无触觉基线高43个百分点，较无预测监督触觉基线高7个百分点。
• 在两个预训练VLA骨干上均提升相关任务，消融显示触觉预测和绝对未来目标关键。

验证范围与局限：验证范围为五项真机任务，涵盖触觉触发 locomotion、持续物理交互、人机接触和移动操作。

英文摘要 (Abstract)

Physical contact often determines how a humanoid should respond during loco-manipulation, yet vision and proprioception alone are often insufficient to characterize physical interaction, especially when the contact region is occluded. Unlike sparse force or torque measurements at predefined regions, distributed tactile sensing preserves spatially resolved contact patterns across the robot body. We therefore study how to integrate such whole-body tactile information into vision-language-action (VLA) policies for contact-rich control. Our approach, Uni-VLaT, introduces a tactile pathway whose latent state is trained not only for action generation, but also to predict future tactile, proprioceptive, and visual representations. This predictive objective builds a tactile-anchored multimodal context, encouraging a more structured understanding of the physical world. We evaluate Uni-VLaT on five real-robot tasks covering tactile-triggered locomotion, sustained physical interaction, human-robot contact, and loco-manipulation. Uni-VLaT achieves a 75% average success rate, outperforming a baseline without tactile input by 43 points and a tactile-input baseline without predictive supervision by 7 points. Across two pretrained VLA backbones, our method improves Table Sweeping by 30 points on both backbones and Back-Tap Walking by 85-90 points. Ablations further show that contextualized tactile prediction and absolute future targets are critical to performance. These results indicate that predictive tactile learning provides an effective route for extending pretrained VLA policies to whole-body physical interaction.

作者：Zihao Wang, Shutong Liu, Siqi Zheng, Liu Cao, Ruoqi Chen, Rundong Liu, Yanchao Yang, Mengdi Xu

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2609.35450：Uni-VLaT: Whole-Body Tactile Adaptation of VLA Policies for Humanoid Loco-Manipulation访问 2026-10-21