# CooHOI: Learning Cooperative Human-Object Interaction with Manipulated Object Dynamics

arXiv: https://arxiv.org/abs/2406.14558 · HTML: https://mlsanity.com/paper/2406.14558/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 46 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Builds on (most cited first, 28 of 28)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Human Motion Diffusion as a Generative Prior](https://mlsanity.com/paper/2303.01418/) (2023)
- [InterGen: Diffusion-based Multi-human Motion Generation under Complex Interactions](https://mlsanity.com/paper/2304.05684/) (2023)
- [Stochastic Scene-Aware Motion Prediction](https://mlsanity.com/paper/2108.08284/) (2021)
- [OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning](https://mlsanity.com/paper/2406.08858/) (2024)
- [Expressive Whole-Body Control for Humanoid Robots](https://mlsanity.com/paper/2402.16796/) (2024)
- [Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion](https://mlsanity.com/paper/2304.01893/) (2023)
- [COUCH: Towards Controllable Human-Chair Interactions](https://mlsanity.com/paper/2205.00541/) (2022)
- [Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation](https://mlsanity.com/paper/2403.04436/) (2024)
- [Synthesizing Diverse Human Motions in 3D Indoor Scenes](https://mlsanity.com/paper/2305.12411/) (2023)
- [Synthesizing Physical Character-Scene Interactions](https://mlsanity.com/paper/2302.00883/) (2023)
- [CALM: Conditional Adversarial Latent Models for Directable Virtual Characters](https://mlsanity.com/paper/2305.02195/) (2023)
- [Unified Human-Scene Interaction via Prompted Chain-of-Contacts](https://mlsanity.com/paper/2309.07918/) (2023)
- [Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing](https://mlsanity.com/paper/2102.07475/) (2021)
- [WoCoCo: Learning Whole-Body Humanoid Control with Sequential Contacts](https://mlsanity.com/paper/2406.06005/) (2024)
- [Deep Imitation Learning for Humanoid Loco-manipulation through Human Teleoperation](https://mlsanity.com/paper/2309.01952/) (2023)
- [Synthesizing Physically Plausible Human Motions in 3D Scenes](https://mlsanity.com/paper/2308.09036/) (2023)
- [PADL: Language-Directed Physics-Based Character Control](https://mlsanity.com/paper/2301.13868/) (2023)
- [Hierarchical Planning and Control for Box Loco-Manipulation](https://mlsanity.com/paper/2306.09532/) (2023)
- [Learning to Sit: Synthesizing Human-Chair Interactions via Hierarchical Control](https://mlsanity.com/paper/1908.07423/) (2019)
- [Simulation and Retargeting of Complex Multi-Character Interactions](https://mlsanity.com/paper/2305.20041/) (2023)
- [Whole-body Humanoid Robot Locomotion with Human Reference](https://mlsanity.com/paper/2402.18294/) (2024)
- [Development of a Whole-body Work Imitation Learning System by a Biped and Bi-armed Humanoid](https://mlsanity.com/paper/2309.15756/) (2023)

## Built on by (most cited first, 30 of 44)

- [ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills](https://mlsanity.com/paper/2502.01143/) (2025)
- [InterMimic: Towards Universal Whole-Body Control for Physics-Based Human-Object Interactions](https://mlsanity.com/paper/2502.20390/) (2025)
- [TokenHSI: Unified Synthesis of Physical Human-Scene Interactions through Task Tokenization](https://mlsanity.com/paper/2503.19901/) (2025)
- [Multi-agent Embodied AI: Advances and Future Directions](https://mlsanity.com/paper/2505.05108/) (2025)
- [HUMOTO: A 4D Dataset of Mocap Human Object Interactions](https://mlsanity.com/paper/2504.10414/) (2025)
- [SkillMimic: Learning Basketball Interaction Skills from Demonstrations](https://mlsanity.com/paper/2408.15270/) (2024)
- [Learning Getting-Up Policies for Real-World Humanoid Robots](https://mlsanity.com/paper/2502.12152/) (2025)
- [A Survey on Human Interaction Motion Generation](https://mlsanity.com/paper/2503.12763/) (2025)
- [SkillMimic-V2: Learning Robust and Generalizable Interaction Skills from Sparse and Noisy Demonstrations](https://mlsanity.com/paper/2505.02094/) (2025)
- [PhysHSI: Towards a Real-World Generalizable and Natural Humanoid-Scene Interaction System](https://mlsanity.com/paper/2510.11072/) (2025)
- [Mimicking-Bench: A Benchmark for Generalizable Humanoid-Scene Interaction Learning via Human Mimicking](https://mlsanity.com/paper/2412.17730/) (2024)
- [SIMS: Simulating Stylized Human-Scene Interactions with Retrieval-Augmented Script Generation](https://mlsanity.com/paper/2411.19921/) (2024)
- [Human-Object Interaction via Automatically Designed VLM-Guided Motion Policy](https://mlsanity.com/paper/2503.18349/) (2025)
- [SViMo: Synchronized Diffusion for Video and Motion Generation in Hand-object Interaction Scenarios](https://mlsanity.com/paper/2506.02444/) (2025)
- [DemoHLM: From One Demonstration to Generalizable Humanoid Loco-Manipulation](https://mlsanity.com/paper/2510.11258/) (2025)
- [InterPrior: Scaling Generative Control for Physics-Based Human-Object Interactions](https://mlsanity.com/paper/2602.06035/) (2026)
- [GenHOI: Generalizing Text-driven 4D Human-Object Interaction Synthesis for Unseen Objects](https://mlsanity.com/paper/2506.15483/) (2025)
- [InterAgent: Physics-based Multi-agent Command Execution via Diffusion on Interaction Graphs](https://mlsanity.com/paper/2512.07410/) (2025)
- [TeamHOI: Learning a Unified Policy for Cooperative Human-Object Interactions with Any Team Size](https://mlsanity.com/paper/2603.07988/) (2026)
- [Decentralized Aerial Manipulation of a Cable-Suspended Load using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2508.01522/) (2025)
- [Task Tokens: A Flexible Approach to Adapting Behavior Foundation Models](https://mlsanity.com/paper/2503.22886/) (2025)
- [InterPose: Learning to Generate Human-Object Interactions from Large-Scale Web Videos](https://mlsanity.com/paper/2509.00767/) (2025)
- [InterReal: A Unified Physics-Based Imitation Framework for Learning Human-Object Interaction Skills](https://mlsanity.com/paper/2603.07516/) (2026)
- [InfBaGel: Human-Object-Scene Interaction Generation with Dynamic Perception and Iterative Refinement](https://mlsanity.com/paper/2604.04843/) (2026)
- [Closed-Loop Vision-Language Planning for Multi-Agent Coordination](https://mlsanity.com/paper/2502.10148/) (2025)
- [Reference-free Human-Object Interaction Editing](https://mlsanity.com/paper/2503.09130/) (2025)
- [Decentralized Real-Time Planning for Multi-UAV Cooperative Manipulation via Imitation Learning](https://mlsanity.com/paper/2510.17143/) (2025)
- [SyncMV4D: Synchronized Multi-view Joint Diffusion of Appearance and Motion for Hand-Object Interaction Synthesis](https://mlsanity.com/paper/2511.19319/) (2025)
- [Leveraging Adaptive Group Negotiation for Heterogeneous Multi-Robot Collaboration with Large Language Models](https://mlsanity.com/paper/2602.06967/) (2025)
- [Learning to Assist: Physics-Grounded Human-Human Control via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2603.11346/) (2026)

## Abstract (arXiv)

Enabling humanoid robots to clean rooms has long been a pursued dream within humanoid research communities. However, many tasks require multi-humanoid collaboration, such as carrying large and heavy furniture together. Given the scarcity of motion capture data on multi-humanoid collaboration and the efficiency challenges associated with multi-agent learning, these tasks cannot be straightforwardly addressed using training paradigms designed for single-agent scenarios. In this paper, we introduce Cooperative Human-Object Interaction (CooHOI), a framework designed to tackle the challenge of multi-humanoid object transportation problem through a two-phase learning paradigm: individual skill learning and subsequent policy transfer. First, a single humanoid character learns to interact with objects through imitation learning from human motion priors. Then, the humanoid learns to collaborate with others by considering the shared dynamics of the manipulated object using centralized training and decentralized execution (CTDE) multi-agent RL algorithms. When one agent interacts with the object, resulting in specific object dynamics changes, the other agents learn to respond appropriately, thereby achieving implicit communication and coordination between teammates. Unlike previous approaches that relied on tracking-based methods for multi-humanoid HOI, CooHOI is inherently efficient, does not depend on motion capture data of multi-humanoid interactions, and can be seamlessly extended to include more participants and a wide range of object types.