CoDance: Learning Reactive and Compliant Human-Humanoid Interaction from Video

Hugging Face Daily Papers Papers
humanoid-robotics human-robot-interaction imitation-learning compliance-control video-learning reinforcement-learning

Summary

CoDance 提出一个从单个双人舞蹈视频中学习反应式、柔顺的人机交互框架，通过多连杆柔顺性增强将运动学演示转化为力感知训练数据，使人形机器人能在物理演示中与人类伙伴完成持续双手舞蹈并灵活调整步态。

Partnered human-humanoid interaction couples locomotion with continuous physical contact. A humanoid needs to coordinate with a person's motion while responding to interaction forces and maintaining stable and natural movement. We present CoDance, a framework for learning reactive and compliant human-humanoid interaction from video. We study partnered dancing as a challenging instantiation, where a humanoid coordinates its footsteps with a moving partner and maintains continuous two-hand contact. Given a single video of two human dancers, CoDance retargets their motions into a robot reference and a moving partner. We introduce a multi-link compliance augmentation that transforms the kinematic demonstration into force-aware training data by adapting the robot reference under structured forces at both hands. Policies trained on this data follow the observed partner while preserving the demonstrated locomotion style and responding compliantly to physical interaction. In simulation, the policies adapt their footsteps to changes in the partner and reproduce approximately 80% of the wrist displacement encoded by the augmented demonstrations. On a physical humanoid, CoDance enables sustained two-hand dancing with a human partner including repeated transitions between forward and backward motions.
Original Article
View Cached Full Text

Cached at: 10/08/26, 03:58 PM

Paper page - CoDance: Learning Reactive and Compliant Human-Humanoid Interaction from Video

Source: https://huggingface.co/papers/2610.05324

Abstract

Partneredhuman-humanoidinteractioncoupleslocomotionwithcontinuousphysicalcontact.Ahumanoidneedstocoordinatewithaperson’smotionwhilerespondingtointeractionforcesandmaintainingstableandnaturalmovement.WepresentCoDance,aframeworkforlearningreactiveandcomplianthuman-humanoidinteractionfromvideo.Westudypartnereddancingasachallenginginstantiation,whereahumanoidcoordinatesitsfootstepswithamovingpartnerandmaintainscontinuoustwo-handcontact.Givenasinglevideooftwohumandancers,CoDanceretargetstheirmotionsintoarobotreferenceandamovingpartner.Weintroduceamulti-linkcomplianceaugmentationthattransformsthekinematicdemonstrationintoforce-awaretrainingdatabyadaptingtherobotreferenceunderstructuredforcesatbothhands.Policiestrainedonthisdatafollowtheobservedpartnerwhilepreservingthedemonstratedlocomotionstyleandrespondingcompliantlytophysicalinteraction.Insimulation,thepoliciesadapttheirfootstepstochangesinthepartnerandreproduceapproximately80%ofthewristdisplacementencodedbytheaugmenteddemonstrations.Onaphysicalhumanoid,CoDanceenablessustainedtwo-handdancingwithahumanpartnerincludingrepeatedtransitionsbetweenforwardandbackwardmotions.

View arXiv pageView PDFProject pageGitHub5Add to collection

Get this paper in your agent:

hf papers read 2610\.05324

Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash

Models citing this paper0

No model linking this paper

Cite arxiv.org/abs/2610.05324 in a model README.md to link it from this page.

Datasets citing this paper1

#### GeneralRoboticsLab/CoDance Updatedabout 1 hour ago • 28

Spaces citing this paper0

No Space linking this paper

Cite arxiv.org/abs/2610.05324 in a Space README.md to link it from this page.

Collections including this paper0

No Collection including this paper

Add this paper to acollectionto link it from this page.

Similar Articles

CoDA: Co-Evolution of Data Engines and Action Policies

Hugging Face Daily Papers

CoDA 提出一种仿真数据引擎与机器人动作策略协同演进的框架，通过共享失败与学习进展记忆、视觉语言诊断以及 Text2potential 分阶段势能奖励，将机器人操作任务的分布外成功率从 5.3% 提升至 27.6%，并在 LIBERO-Pro 上取得显著改善。

Learning Transferable World-Action Models from Task-Paired Human Videos

Hugging Face Daily Papers

该论文提出WATCH，一种跨视频预训练方法，通过任务配对的人类演示学习可迁移的世界-动作模型，无需人-机器人配对数据；在机器人后训练后将MimicDroid的上下文内成功率从40.9%提升至68.3%（Cosmos Policy）以及从59.8%提升至77.7%（Cosmos 3）。