CoHuB: A Simulation Benchmark for Multi-Humanoid Collaboration

arXiv:2609.34782 · cs.RO, cs.AI, cs.CV, cs.LG · Submitted 2026-09-28 · Read on arXiv

cs.RO, cs.AI, cs.CV, cs.LG

Submitted: 2026-09-28

Updated: 2026-09-28

Code: https://github.com/pink-kinematics/pink

Project page: https://nvlabs.github.io/GR00T-WholeBodyControl

Terminology

• Benchmark — 43× in this paper · explained in 2 episodes
• World Action Model — 12× in this paper
• Benchmarking — 6× in this paper · explained in 3 episodes
• Foundation Model — 6× in this paper · explained in 3 episodes
• Vision-Language-Action (VLA) Models — 6× in this paper · explained in 2 episodes
• Action Space — 5× in this paper · explained in 1 episode
• Foundation Models — 5× in this paper · explained in 7 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Multi-agent Collaboration — 3× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• World Action Model (WAM) — 2× in this paper · explained in 3 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Action Chunking — 1× in this paper · explained in 1 episode
• Action Chunking with Transformer — 1× in this paper
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Agent Interactions — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Imitation Learning (IL) — 1× in this paper · explained in 2 episodes
• Multi-Agent Reinforcement Learning (MARL) — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
• CHORUS: Decentralized Multi-Embodiment Collaboration with One VLA Policy
• It Takes Two: Learning Interactive Whole-Body Control Between Humanoid Robots
• Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning
• Wan: Open and Advanced Large-Scale Video Generative Models
• HumanoidArena: Benchmarking Egocentric Hierarchical Whole-body Learning
• SIMPLE: Simulation-Based Policy Learning and Evaluation for Humanoid Loco-manipulation
• LeVERB: Humanoid Whole-Body Control with Latent Vision-Language Instruction
• World Action Models are Zero-shot Policies
• Fast-WAM: Do World Action Models Need Test-time Future Imagination?
• AGILE: A Comprehensive Workflow for Humanoid Loco-Manipulation Learning
• MotionWAM: Towards Foundation World Action Models for Real-Time Humanoid Loco-Manipulation
• robosuite: A Modular Simulation Framework and Benchmark for Robot Learning

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