仅用2分钟目标域数据，让机器人快速适应新环境下的精确控制。
FADA: Few-Shot Domain Adaptation via Dynamics Alignment for Humanoid Control
- 分三阶段训练，通过动作与观测对齐实现动力学适配。
- 仅需约2分钟真实数据微调，性能超越现有少样本方法。
- 适合需要快速部署的复杂人形机器人任务场景。
高精度人形机器人控制受限于目标域动力学不匹配问题，即在地形、负载或执行器响应变化时，相同控制目标会引发不同运动表现。现有方法或依赖领域随机化实现零样本迁移，或需大量目标域数据进行模型校准、残差学习或策略重训。本文提出FADA（Few-Shot Domain Adaptation via Dynamics Alignment），一种三阶段规划-逆动力学模型（Planner-IDM）框架，用于人形控制中的少样本域适应。FADA首先利用特权信息训练一个最优策略，再通过DAgger将该策略行为蒸馏为可部署的规划-逆动力学学生模型。部署时，冻结规划器，仅使用约2分钟的目标域轨迹数据，以标准监督学习微调逆动力学模型。FADA不依赖最优示范或奖励信号，而是直接使用滚动过程中观测到的动作与状态对作为监督，使逆动力学模型生成的动作与目标域动力学对齐。实验表明，FADA优于基于上下文和端到端的适应基线，在动力学变化下提升任务性能，成功驱动真实人形机器人完成多样化的高精度全身任务。实现细节及硬件演示视频见 https://lecar-lab.github.io/FADA-humanoid/。
原文摘要 · Abstract (English)
High-precision humanoid control is limited by target-domain dynamics mismatch, where the same control objective can induce different realized motions under changes in terrain, payload, or actuator response. Existing methods either pursue zero-shot transfer through domain randomization or in-context adaptation without target-domain specialization, or require heavy adaptation pipelines that leverage target-domain data, such as model calibration, residual learning, or policy retraining. In this paper, we present FADA (Few-Shot Domain Adaptation via Dynamics Alignment), a three-stage Planner-Inverse Dynamics Model (Planner-IDM) framework for few-shot adaptation in humanoid control. FADA first trains an oracle policy with privileged information and then distills the oracle behavior into a deployable Planner-IDM student through DAgger. At deployment, FADA freezes the planner and finetunes only the IDM using approximately 2 minutes of target-domain rollouts with standard supervised learning. Rather than requiring optimal demonstrations or rewards, FADA uses the paired actions and observations that are observed during these rollouts as supervision, aligning the IDM's action generation with target-domain dynamics. Experiments show that FADA outperforms both in-context and end-to-end adaptation baselines, improving task performance under dynamics shifts and enabling real humanoid robots to execute diverse high-precision whole-body tasks. Implementation details and qualitative hardware rollout videos are available at https://lecar-lab.github.io/FADA-humanoid/.
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