CoHuB: A Simulation Benchmark for Multi-Humanoid Collaboration
cs.RO, cs.AI, cs.CV, cs.LG
Submitted: 2026-09-28
Updated: 2026-09-28
Comments: Project page: https://meat124.github.io/CoHuB/
Code: https://github.com/pink-kinematics/pink
Project page: https://nvlabs.github.io/GR00T-WholeBodyControl
License: http://creativecommons.org/licenses/by/4.0/
The gist: Many physical tasks in human environments require collaboration, from assisting a partner to jointly manipulating an object.
Terminology
Abstract
Many physical tasks in human environments require collaboration, from assisting a partner to jointly manipulating an object. Yet, existing humanoid benchmarks largely focus on single-humanoid skills and lack evaluation of multi-humanoid collaboration under egocentric visual observations. We introduce CoHuB (Collaborative Multi-Humanoid Benchmark), a simulation benchmark for multi-humanoid collaboration under egocentric visual observations. CoHuB provides 10 tasks, eight with two humanoids and two with three humanoids, spanning diverse collaboration patterns. We also provide synchronized demonstrations collected through a multi-operator VR teleoperation pipeline, in which each operator controls one humanoid from its egocentric view. Experiments with representative visuomotor policies reveal substantial challenges across different forms of coordinated perception and control. CoHuB provides a foundation for developing and evaluating multi-humanoid collaboration policies.
Sources
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
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- It Takes Two: Learning Interactive Whole-Body Control Between Humanoid Robots
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- LeVERB: Humanoid Whole-Body Control with Latent Vision-Language Instruction
- World Action Models are Zero-shot Policies
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