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arXiv · 2602.21723

LessMimic: Long-Horizon Humanoid Interaction with Unified Distance Field Representations

Yutang Lin·Jieming Cui·Yixuan Li·Baoxiong Jia·Yixin Zhu·Siyuan Huang
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Abstract

Humanoid robots that autonomously interact with physical environments over extended horizons represent a central goal of embodied intelligence. Existing approaches rely on reference motions or task-specific rewards, tightly coupling policies to particular object geometries and precluding multi-skill generalization within a single framework. A unified interaction representation enabling reference-free inference, geometric generalization, and long-horizon skill composition within one policy remains an open challenge. Here we show that Distance Field (DF) provides such a representation: LessMimic conditions a single whole-body policy on DF-derived geometric cues--surface distances, gradients, and velocity decompositions--removing the need for motion references, with interaction latents encoded via a Variational Auto-Encoder (VAE) and post-trained using Adversarial Interaction Priors (AIP) under Reinforcement Learning (RL). Through DAgger-style distillation that aligns DF latents with egocentric depth features, LessMimic further transfers seamlessly to vision-only deployment without motion capture (MoCap) infrastructure. A single LessMimic policy achieves 80--100% success across object scales from 0.4x to 1.6x on PickUp and SitStand where baselines degrade sharply, attains 62.1% success on 5 task instances trajectories, and remains viable up to 40 sequentially composed tasks. By grounding interaction in local geometry rather than demonstrations, LessMimic offers a scalable path toward humanoid robots that generalize, compose skills, and recover from failures in unstructured environments.

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Yutang Lin, Jieming Cui, Yixuan Li, Baoxiong Jia, Yixin Zhu, Siyuan Huang. 2026-02-25. LessMimic: Long-Horizon Humanoid Interaction with Unified Distance Field Representations. https://arxiv.org/abs/2602.21723

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