aiqbench analysis
This paper tackles sim-to-real dynamics mismatch for humanoid robots with a learning pipeline: a privileged-information oracle is distilled into a deployable student via DAgger, then only the inverse dynamics module is finetuned on about two minutes of target-domain rollouts. The reported gains are over in-context and end-to-end adaptation baselines, with real-hardware demonstrations, but the abstract alone doesn't quantify the margins or the range of shifts tested. For ML readers, the practical takeaway is that freezing a planner and adapting a small inverse dynamics model with supervised learning on unlabeled rollout data is a cheap adaptation strategy worth evaluating against zero-shot and full-retraining approaches.
FADA: Few-Shot Domain Adaptation via Dynamics Alignment for Humanoid Control
arXiv:2606.28476v2 Announce Type: replace Abstract: 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/.
Abstract: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 this https URL.
Robotics (cs.RO)
arXiv:2606.28476 [cs.RO]
(or arXiv:2606.28476v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.28476 Focus to learn more arXiv-issued DOI via DataCite
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