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

arXiv: https://arxiv.org/abs/2610.05324 · HTML: https://mlsanity.com/paper/2610.05324/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 27 references that are in the map.
- First posted on arXiv 4 Oct 2026.

## Builds on (most cited first, 21 of 21)

- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning](https://mlsanity.com/paper/2406.08858/) (2024)
- [BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion](https://mlsanity.com/paper/2508.08241/) (2025)
- [Expressive Whole-Body Control for Humanoid Robots](https://mlsanity.com/paper/2402.16796/) (2024)
- [Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation](https://mlsanity.com/paper/2403.04436/) (2024)
- [ExBody2: Advanced Expressive Humanoid Whole-Body Control](https://mlsanity.com/paper/2412.13196/) (2024)
- [PromptHMR: Promptable Human Mesh Recovery](https://mlsanity.com/paper/2504.06397/) (2025)
- [Retargeting Matters: General Motion Retargeting for Humanoid Motion Tracking](https://mlsanity.com/paper/2510.02252/) (2025)
- [mjlab: A Lightweight Framework for GPU-Accelerated Robot Learning](https://mlsanity.com/paper/2601.22074/) (2026)
- [SoftMimic: Learning Compliant Whole-body Control from Examples](https://mlsanity.com/paper/2510.17792/) (2025)
- [HumanX: Toward Agile and Generalizable Humanoid Interaction Skills from Human Videos](https://mlsanity.com/paper/2602.02473/) (2026)
- [FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots](https://mlsanity.com/paper/2505.06883/) (2025)
- [Learning Human-Humanoid Coordination for Collaborative Object Carrying](https://mlsanity.com/paper/2510.14293/) (2025)
- [GentleHumanoid: Learning Upper-body Compliance for Contact-rich Human and Object Interaction](https://mlsanity.com/paper/2511.04679/) (2025)
- [Learning Whole-Body Human-Humanoid Interaction from Human-Human Demonstrations](https://mlsanity.com/paper/2601.09518/) (2026)
- [Learning a Unified Policy for Position and Force Control in Legged Loco-Manipulation](https://mlsanity.com/paper/2505.20829/) (2025)
- [DFM: Deep Fourier Mimic for Expressive Dance Motion Learning](https://mlsanity.com/paper/2502.10980/) (2025)
- [H2-COMPACT: Human-Humanoid Co-Manipulation via Adaptive Contact Trajectory Policies](https://mlsanity.com/paper/2505.17627/) (2025)
- [Rhythm: Learning Interactive Whole-Body Control for Dual Humanoids](https://mlsanity.com/paper/2603.02856/) (2026)
- [CEER: Compliant End-Effector and Root Control as a Unified Interface for Hierarchical Humanoid Loco-Manipulation](https://mlsanity.com/paper/2605.19981/) (2026)
- [Dancing with REEM-C: A robot-to-human physical-social communication study](https://mlsanity.com/paper/2408.05301/) (2024)

## Abstract (arXiv)

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.