# Learning Multi-Humanoid Pickup and Transport via Decentralized Object-Centric Control

arXiv: https://arxiv.org/abs/2609.17824 · HTML: https://mlsanity.com/paper/2609.17824/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 29 references that are in the map.
- First posted on arXiv 15 Sep 2026.

## Builds on (most cited first, 22 of 22)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments](https://mlsanity.com/paper/2301.04195/) (2023)
- [Sim-to-Real Learning of All Common Bipedal Gaits via Periodic Reward Composition](https://mlsanity.com/paper/2011.01387/) (2020)
- [CooHOI: Learning Cooperative Human-Object Interaction with Manipulated Object Dynamics](https://mlsanity.com/paper/2406.14558/) (2024)
- [ResMimic: From General Motion Tracking to Humanoid Whole-body Loco-Manipulation via Residual Learning](https://mlsanity.com/paper/2510.05070/) (2025)
- [Opt2Skill: Imitating Dynamically-feasible Whole-Body Trajectories for Versatile Humanoid Loco-Manipulation](https://mlsanity.com/paper/2409.20514/) (2024)
- [Sim-to-Real Learning for Humanoid Box Loco-Manipulation](https://mlsanity.com/paper/2310.03191/) (2023)
- [Learning Multi-Modal Whole-Body Control for Real-World Humanoid Robots](https://mlsanity.com/paper/2408.07295/) (2024)
- [Learning Human-Humanoid Coordination for Collaborative Object Carrying](https://mlsanity.com/paper/2510.14293/) (2025)
- [TeamHOI: Learning a Unified Policy for Cooperative Human-Object Interactions with Any Team Size](https://mlsanity.com/paper/2603.07988/) (2026)
- [Learning Decentralized Multi-Biped Control for Payload Transport](https://mlsanity.com/paper/2406.17279/) (2024)
- [Multi-Quadruped Cooperative Object Transport: Learning Decentralized Pinch-Lift-Move](https://mlsanity.com/paper/2509.14342/) (2025)
- [Cooperative Grasping and Transportation using Multi-agent Reinforcement Learning with Ternary Force Representation](https://mlsanity.com/paper/2411.13942/) (2024)
- [Sumo: Dynamic and Generalizable Whole-Body Loco-Manipulation](https://mlsanity.com/paper/2604.08508/) (2026)
- [SplitAdapter: Load-Aware Humanoid Loco-Manipulation via Factorized Adaptation](https://mlsanity.com/paper/2606.03297/) (2026)
- [PAINT: Partner-Agnostic Intent-Aware Cooperative Transport with Legged Robots](https://mlsanity.com/paper/2604.12852/) (2026)
- [PACC: A Passive-Arm Approach for High-Payload Collaborative Carrying with Quadruped Robots Using Model Predictive Control](https://mlsanity.com/paper/2403.19862/) (2024)
- [Embracing Bulky Objects with Humanoid Robots: Whole-Body Manipulation with Reinforcement Learning](https://mlsanity.com/paper/2509.13534/) (2025)
- [Shared Object Manipulation with a Team of Collaborative Quadrupeds](https://mlsanity.com/paper/2510.00682/) (2025)
- [ACLM: ADMM-Based Distributed Model Predictive Control for Collaborative Loco-Manipulation](https://mlsanity.com/paper/2603.07095/) (2026)
- [DeReCo: Decoupling Representation and Coordination Learning for Object-Adaptive Decentralized Multi-Robot Cooperative Transport](https://mlsanity.com/paper/2603.08111/) (2026)

## Abstract (arXiv)

We study cooperative multi-humanoid pickup and transport of objects with varying size, weight, and geometry, requiring robot teams of different sizes. Our approach uses decentralized object-centric control, where each humanoid is assigned a local attachment region on the shared object and learns to realize pickup and transport through gripperless bimanual pinching. This attachment-based interface provides a common control abstraction spanning single-robot pickup, cooperative multi-robot transport, and robot-to-robot handover, without per-task redesign. We find that policies trained only on single-robot pickup already transfer nontrivially to cooperative settings, suggesting that this abstraction captures much of the structure needed for coordination. At the same time, explicit multi-robot training further improves performance, showing that shared-object coupling introduces coordination dynamics that are beneficial to learn directly. We validate the approach in simulation across varying team sizes and object geometries, and demonstrate sim-to-real transfer on hardware, where the learned controllers enable real humanoids to perform cooperative manipulation tasks.