2026-06-30 04:00 UTCOriginal source2 min readUpdated: 2026-06-30 08:24 UTC

FADA: Few-Shot Domain Adaptation via Dynamics Alignment for Humanoid Control

arXiv paper introduces FADA, a few-shot domain adaptation framework for humanoid control that aligns dynamics with minimal target-domain data.

SourcearXiv RoboticsAuthor: Angchen Xie, Nikhil Sobanbabu, Ishayu Shikhare, Alan Wang, Max Simchowitz, Guanya Shi

[2606.28476] FADA: Few-Shot Domain Adaptation via Dynamics Alignment for Humanoid Control

[Submitted on 26 Jun 2026]

Title:FADA: Few-Shot Domain Adaptation via Dynamics Alignment for Humanoid Control

View a PDF of the paper titled FADA: Few-Shot Domain Adaptation via Dynamics Alignment for Humanoid Control, by Angchen Xie and 5 other authors

View PDF HTML (experimental)

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.

Comments: Project page: this https URL

Subjects:

Robotics (cs.RO)

Cite as: arXiv:2606.28476 [cs.RO]

(or arXiv:2606.28476v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2606.28476

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Angchen Xie [view email] [v1] Fri, 26 Jun 2026 16:05:10 UTC (65,047 KB)

Full-text links:

Access Paper:

View a PDF of the paper titled FADA: Few-Shot Domain Adaptation via Dynamics Alignment for Humanoid Control, by Angchen Xie and 5 other authors

View PDF

HTML (experimental)

TeX Source

view license

Current browse context:

cs.RO

new | recent | 2026-06

Change to browse by:

References & Citations

NASA ADS

Google Scholar

Semantic Scholar

Data provided by:

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer (What is the Explorer?)

Connected Papers Toggle

Connected Papers (What is Connected Papers?)

Litmaps Toggle

Litmaps (What is Litmaps?)

scite.ai Toggle

scite Smart Citations (What are Smart Citations?)

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv (What is alphaXiv?)

Links to Code Toggle

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub Toggle

DagsHub (What is DagsHub?)

GotitPub Toggle

Gotit.pub (What is GotitPub?)

Huggingface Toggle

Hugging Face (What is Huggingface?)

ScienceCast Toggle

ScienceCast (What is ScienceCast?)

Demos

Replicate Toggle

Replicate (What is Replicate?)

Spaces Toggle

Hugging Face Spaces (What is Spaces?)

Spaces Toggle

TXYZ.AI (What is TXYZ.AI?)