WT-UMI: Tactile-based Whole-Body Manipulation via Force-Supervised Contact-Aware Planning

Jaehwi Jang, Zhaoyuan Gu, Alfred Cueva, Zimeng Chai, Junjie Sheng, Thong Nguyen, Himank Galundia, Yifan Wu, Huishu Xue, Isaac Legene, Ojas Mediratta, Davin Doan, Andrew Collins, Sarah Sadegh, KyoungMok Kim, Rishita Dhalbisoi, Zun Chen, Ye Zhao · N/A · 2026

Summary

Abstract Summary

Whole-body humanoid manipulation of bulky, deformable, and shared-load objects requires distributed contact sensing and explicit force regulation, yet most imitation policies treat contact force only implicitly. On the other hand, different demonstration sources provide complementary modalities with inherent trade-offs: human demonstrations capture natural contact forces but not robot-executable actions, while teleoperation directly records robot actions but with less natural force regulation. This paper presents WT-UMI, a wearable whole-body tactile interface worn by human operators or mounted on humanoids, providing accurate observations of tactile images, contact forces, and end-effector poses across both human demonstration and humanoid teleoperation modes. We introduce a force-conditioned target-pose correction module that converts measured human poses into contact-aware robot targets by learning corrections from teleoperation data. To leverage the natural force interaction in human data, we propose a force-supervised planner that predicts end-effector pose chunks and contact-force trajectories. The predicted contact force serves as the reference for a tactile-based admittance controller. Across five contact-rich tasks spanning deformable objects, bulky rigid objects, and human--humanoid collaboration, WT-UMI improves success rate and reduces contact-position tracking error over four policy baselines. Our project page is available at https://wt-umi.github.io/WTUMI/.

Key Points

Introduces WT-UMI, a wearable whole-body tactile interface for humanoid manipulation.
Proposes a force-conditioned target-pose correction module to convert human poses into contact-aware robot targets.
Achieves superior performance on contact-rich tasks including deformable objects and human-humanoid collaboration.
Demonstrates the importance of explicit force regulation in whole-body manipulation policies.

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humanoid reinforcement-learning model-predictive-control locomotion manipulation

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GitHub ★ — Jun 2026

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