Learning Tactile-Aware Quadrupedal Loco-Manipulation Policies

• URL: http://arxiv.org/abs/2604.27224v1
• Date: Wed, 29 Apr 2026 21:46:58 GMT
• Title: Learning Tactile-Aware Quadrupedal Loco-Manipulation Policies
• Abstract summary: We present a tactile-aware loco-manipulation policy learning pipeline with a hierarchical structure.<n>First, we leverage real-world human demonstrations to train a tactile-conditioned visuotactile high-level policy.<n>Second, we perform large-scale reinforcement learning in simulation to learn a tactile-aware whole-body control policy.
• Score: 33.25194785586049
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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