GaitSpan：将类人机器人的运动从行走扩展到跑步
GaitSpan: Growing Humanoid Locomotion from Walking to Running
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中文总结 AI 辅助
研究如何让类人机器人从行走扩展到跑步，提出GaitSpan框架，将预训练行走策略扩展，通过节奏生成、步幅塑造和残差适应三方面实现，能跨形态、跨地形零样本部署，相比基线学习更快且步态性能更强。
中文摘要 AI 辅助
一个能行走的类人机器人不应从头开始重新学习慢跑或跑步的运动。当前方法通常通过规定步态计划、模仿运动片段、训练专家切换或提炼技能为单一策略来获得步态多样性。这些策略虽能产生令人印象深刻的行为，但在连续速度指令、地形和形态方面灵活性有限。我们用GaitSpan研究技能增长，它将预训练的基本行走策略扩展为更快的运动。它把行走视为种子技能，可在新节奏下再生、扩展步幅并通过残差适应修正。这种扩展有节奏生成、步幅塑造和残差适应三个方面。GaitSpan首次提供了一个单一的指令条件类人机器人策略，涵盖行走、慢跑和类似跑步的状态，跨形态转移，并能在未见的模拟到模拟和真实世界地形上零样本部署。与多专家训练或人类模仿的基线相比，它学习更快，步态性能更强。
英文摘要
A humanoid that can walk should not relearn locomotion from scratch to jog or run. Yet current approaches often obtain gait diversity by prescribing gait schedules, imitating motion clips, training experts to switch between or distilling skills into one policy. These strategies can produce impressive behaviors, but offer limited flexibility across continuous speed commands, terrains, and morphologies. We study skill growth with GaitSpan, a framework that expands a pretrained, basic walking policy into faster locomotion. It treats walking as a seed skill: reusable motor structure for balance, support, body coordination, and contact transition that can be regenerated at new rhythms, extended into longer/higher strides, and corrected by residual adaptation. This expansion has three aspects: 1) rhythm generation, which modulates the frozen walking policy with multiple internal clocks and learns command-conditioned combinations of the resulting canonical actions; 2) stride shaping, which rewards dynamic locomotion patterns appropriate for higher commanded speeds using a physically grounded objective inspired by spring-loaded inverted pendulum dynamics; and 3) residual adaptation, which captures motion details not accounted for by rhythm generation or stride shaping. GaitSpan is the first to deliver a single command-conditioned humanoid policy that spans walking, jogging, and running-like regimes covering a continuous speed range, transfers across morphologies, and deploys zero-shot on unseen sim-to-sim, and real-world terrains. Compared with baselines either trained with multi-experts or imitation from humans, it learns faster and achieves stronger gait performance.
发表机构
- University of Michigan（密歇根大学）
- UC Berkeley（加州大学伯克利分校）
机构由 AI 辅助整理，请以论文原文为准。
