用可微仿真解决人形机器人重载时的仿真到现实差距问题
HALO:Closing Sim-to-Real Gap for Heavy-loaded Humanoid Agile Motion Skills via Differentiable Simulation
- 基于可微仿真器分两阶段识别机器人参数和未知负载质量分布
- 在重载条件下实现强化学习策略零样本迁移，运动追踪误差更低
- 适合研究仿真到现实迁移、人形机器人控制的科研人员
实际应用中，人形机器人常需携带未知负载，导致仿真与现实间存在显著差异，削弱强化学习方法的有效性。为此，我们提出一种基于可微仿真器MuJoCo XLA的两阶段梯度驱动系统辨识框架。第一阶段利用真实数据校准机器人本体模型，减少仿真中的固有偏差；第二阶段进一步识别未知负载的质量分布。通过在策略训练前显式消除结构化模型偏差，该方法实现了重载条件下强化学习策略的零样本硬件迁移。大量仿真与实机实验表明，相比现有基线，本方法具备更精确的参数辨识能力、更高的运动追踪精度，以及显著增强的敏捷性与鲁棒性。
原文摘要 · Abstract (English)
Humanoid robots deployed in real-world scenarios often need to carry unknown payloads, which introduce significant mismatch and degrade the effectiveness of simulation-to-reality reinforcement learning methods. To address this challenge, we propose a two-stage gradient-based system identification framework built on the differentiable simulator MuJoCo XLA. The first stage calibrates the nominal robot model using real-world data to reduce intrinsic sim-to-real discrepancies, while the second stage further identifies the mass distribution of the unknown payload. By explicitly reducing structured model bias prior to policy training, our approach enables zero-shot transfer of reinforcement learning policies to hardware under heavy-load conditions. Extensive simulation and real-world experiments demonstrate more precise parameter identification, improved motion tracking accuracy, and substantially enhanced agility and robustness compared to existing baselines. Project Page: https://mwondering.github.io/halo-humanoid/
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