Colosseum V2: Benchmarking Generalization for Vision-Language-Action Models

arXiv:2605.27759 · cs.RO · Submitted 2026-05-26 · Read on arXiv

cs.RO

Submitted: 2026-05-26

Updated: 2026-09-29

Comments: Accepted to IEEE Robotics and Automation Letters (RA-L)

Code: https://github.com/huggingface/lerobot

Project page: https://colosseum-v2.github.io

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Generalization — 45× in this paper · explained in 13 episodes
• Benchmark — 42× in this paper · explained in 2 episodes
• Robustness — 18× in this paper · explained in 5 episodes
• Benchmarking — 8× in this paper · explained in 3 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• VLA Models — 5× in this paper · explained in 3 episodes
• Distribution Shift — 4× in this paper · explained in 5 episodes
• Action Chunking — 3× in this paper · explained in 1 episode
• Action Chunking Transformers — 3× in this paper
• Action Space — 3× in this paper · explained in 1 episode
• Sim-to-Real Gap — 3× in this paper · explained in 6 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Actor — 1× in this paper · explained in 3 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Compositional Generalization — 1× in this paper · explained in 4 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Manipulation Object — 1× in this paper
• Multimodal Inputs — 1× in this paper · explained in 2 episodes
• Procedural Knowledge — 1× in this paper · explained in 2 episodes
• Receiving Object — 1× in this paper
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Sim-to-Real Transfer — 1× in this paper · explained in 2 episodes
• Vision-Language-Action (VLA) Models — 1× in this paper · explained in 2 episodes

Sources

• SAM 2: Segment Anything in Images and Videos
• robosuite: A Modular Simulation Framework and Benchmark for Robot Learning
• VLABench: A Large-Scale Benchmark for Language-Conditioned Robotics Manipulation with Long-Horizon Reasoning Tasks
• VLMbench: A Compositional Benchmark for Vision-and-Language Manipulation
• LIBERO-Para: A Diagnostic Benchmark and Metrics for Paraphrase Robustness in VLA Models
• ManipBench: Benchmarking Vision-Language Models for Low-Level Robot Manipulation
• VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training
• PaLM-E: An Embodied Multimodal Language Model
• RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
• OpenVLA: An Open-Source Vision-Language-Action Model
• Provable Preconditioned Plug-and-Play Approach for Compressed Sensing MRI Reconstruction
• $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
• Learning Transferable Visual Models From Natural Language Supervision
• Sigmoid Loss for Language Image Pre-Training
• MolmoAct: Action Reasoning Models that can Reason in Space
• VIMA: General Robot Manipulation with Multimodal Prompts

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