# Learning Human-Humanoid Coordination for Collaborative Object Carrying

arXiv: https://arxiv.org/abs/2510.14293 · HTML: https://mlsanity.com/paper/2510.14293/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 32 references that are in the map.
- First posted on arXiv 16 Oct 2025.

## Builds on (most cited first, 18 of 18)

- [TWIST: Teleoperated Whole-Body Imitation System](https://mlsanity.com/paper/2505.02833/) (2025)
- [Humanoid Parkour Learning](https://mlsanity.com/paper/2406.10759/) (2024)
- [Humanoid Locomotion and Manipulation: Current Progress and Challenges in Control, Planning, and Learning](https://mlsanity.com/paper/2501.02116/) (2025)
- [Generalizable Humanoid Manipulation with 3D Diffusion Policies](https://mlsanity.com/paper/2410.10803/) (2024)
- [Closed-Loop Open-Vocabulary Mobile Manipulation with GPT-4V](https://mlsanity.com/paper/2404.10220/) (2024)
- [FALCON: Learning Force-Adaptive Humanoid Loco-Manipulation](https://mlsanity.com/paper/2505.06776/) (2025)
- [CLONE: Closed-Loop Whole-Body Humanoid Teleoperation for Long-Horizon Tasks](https://mlsanity.com/paper/2506.08931/) (2025)
- [HuB: Learning Extreme Humanoid Balance](https://mlsanity.com/paper/2505.07294/) (2025)
- [MimicDroid: In-Context Learning for Humanoid Robot Manipulation from Human Play Videos](https://mlsanity.com/paper/2509.09769/) (2025)
- [FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots](https://mlsanity.com/paper/2505.06883/) (2025)
- [StyleLoco: Generative Adversarial Distillation for Natural Humanoid Robot Locomotion](https://mlsanity.com/paper/2503.15082/) (2025)
- [Do You Need a Hand? -- a Bimanual Robotic Dressing Assistance Scheme](https://mlsanity.com/paper/2301.02749/) (2023)
- [Learning Part Motion of Articulated Objects Using Spatially Continuous Neural Implicit Representations](https://mlsanity.com/paper/2311.12407/) (2023)
- [H2-COMPACT: Human-Humanoid Co-Manipulation via Adaptive Contact Trajectory Policies](https://mlsanity.com/paper/2505.17627/) (2025)
- [Vision- and tactile-based continuous multimodal intention and attention recognition for safer physical human-robot interaction](https://mlsanity.com/paper/2206.11350/) (2022)
- [Human-Multirobot Collaborative Mobile Manipulation: the Omnid Mocobots](https://mlsanity.com/paper/2206.14293/) (2022)
- [Human-Like Robot Impedance Regulation Skill Learning from Human-Human Demonstrations](https://mlsanity.com/paper/2502.13707/) (2025)
- [IDAGC: Adaptive Generalized Human-Robot Collaboration via Human Intent Estimation and Multimodal Policy Learning](https://mlsanity.com/paper/2507.04620/) (2025)

## Built on by (most cited first, 7 of 7)

- [SteadyTray: Learning Object Balancing Tasks in Humanoid Tray Transport via Residual Reinforcement Learning](https://mlsanity.com/paper/2603.10306/) (2026)
- [Human Motion Estimation with Everyday Wearables](https://mlsanity.com/paper/2512.21209/) (2025)
- [Cognition to Control - Multi-Agent Learning for Human-Humanoid Collaborative Transport](https://mlsanity.com/paper/2603.03768/) (2026)
- [Learning Multi-Humanoid Pickup and Transport via Decentralized Object-Centric Control](https://mlsanity.com/paper/2609.17824/) (2026)
- [Learning from Humans for Proactive Assistance in Human-Robot Collaborative Transport](https://mlsanity.com/paper/2609.25351/) (2026)
- [Humanoid Rickshaw Pulling: Whole-Body Locomotion under Coupled Wheeled Loads](https://mlsanity.com/paper/2610.04238/) (2026)
- [CoDance: Learning Reactive and Compliant Human-Humanoid Interaction from Video](https://mlsanity.com/paper/2610.05324/) (2026)

## Abstract (arXiv)

Human-humanoid collaboration shows significant promise for applications in healthcare, domestic assistance, and manufacturing. While compliant robot-human collaboration has been extensively developed for robotic arms, enabling compliant human-humanoid collaboration remains largely unexplored due to humanoids' complex whole-body dynamics. In this paper, we propose a proprioception-only reinforcement learning approach, COLA, that combines leader and follower behaviors within a single policy. The model is trained in a closed-loop environment with dynamic object interactions to predict object motion patterns and human intentions implicitly, enabling compliant collaboration to maintain load balance through coordinated trajectory planning. We evaluate our approach through comprehensive simulator and real-world experiments on collaborative carrying tasks, demonstrating the effectiveness, generalization, and robustness of our model across various terrains and objects. Simulation experiments demonstrate that our model reduces human effort by 24.7%. compared to baseline approaches while maintaining object stability. Real-world experiments validate robust collaborative carrying across different object types (boxes, desks, stretchers, etc.) and movement patterns (straight-line, turning, slope climbing). Human user studies with 23 participants confirm an average improvement of 27.4% compared to baseline models. Our method enables compliant human-humanoid collaborative carrying without requiring external sensors or complex interaction models, offering a practical solution for real-world deployment.