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Learning Tactile-Aware Quadrupedal Loco-Manipulation Policies
[Submitted on 29 Apr 2026 (v1), last revised 12 Jul 2026 (this v · 2026-04-30 · via cs.RO updates on arXiv.org

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Abstract:Quadrupedal loco-manipulation is commonly built on visual perception and proprioception. Yet reliable contact-rich manipulation remains difficult: vision and proprioception alone cannot resolve uncertain, evolving interactions with the environment. Tactile sensing offers direct contact observability, but scalable tactile-aware learning framework for quadrupedal loco-manipulation is still underexplored. In this paper, we present a tactile-aware loco-manipulation policy learning pipeline with a hierarchical structure. Our approach has two key components. First, we leverage real-world human demonstrations to train a tactile-conditioned visuotactile high-level policy. This policy predicts not only end-effector trajectories for manipulation, but also the evolving tactile interaction cues that characterize how contact should develop over time. Second, we perform large-scale reinforcement learning in simulation to learn a tactile-aware whole-body control policy that tracks diverse commanded trajectories and tactile interaction cues, and transfers zero-shot to the real world. Together, these components enable coordinated locomotion and manipulation under contact-rich scenarios. We evaluate the system on real-world contact-rich tasks, including in-hand reorientation with insertion, valve tightening, and delicate object manipulation. Compared to vision-only and visuotactile baselines, our method improves performance by 28.54% on average across these tasks.

Submission history

From: Pokuang Zhou [view email]
[v1] Wed, 29 Apr 2026 21:46:58 UTC (8,971 KB)
[v2] Sun, 10 May 2026 23:19:30 UTC (8,974 KB)
[v3] Sun, 12 Jul 2026 18:01:47 UTC (8,959 KB)

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