RoboColosseum tests robot skills across many machines and tasks
RoboColosseum: Evaluating Physical Intelligence at Scale In-The-Wild
RoboticsArtificial Intelligence
Summary
Robots need to work well on many tasks and machines in different places. The researchers created RoboColosseum, a system that lets many robots be tested under the same rules but in different locations. They compared how well different robot brain models performed on various tasks before and after learning from examples. The tests showed that many robots still struggle with complex tasks and that success rankings can change a lot depending on the task and machine. This helps understand where robot skills are strong, how well they can improve, and how close they get to completing difficult tasks.
What this means in practice
- •For robotics development teams: Evaluate and compare robot control algorithms on many kinds of robots and tasks under consistent conditions.
- •For robotics hardware manufacturers: Identify how well different robot models support AI policies and where they need improvement in real-world scenarios.
Authors
Tan Sang Nguyen, Yi Ru Wang, Shailesh Shailesh, Gang Yang, Jiamo Hu, Jianuo Jiang, Haoquan Fang, Zhongzheng Ren, Zhongyi Luo, Ian Chong, Aaron Tung, Santiago Pravisani, Minghui Lin, Yao Mo, Ishneet Singh, Benjamin Tee, Harold Soh, Siddhartha Srinivasa, Dieter Fox, David Hsu, Haoming Song, Maxime Ellerbach, Thomas Wolf, Pepijn Kooijmans, Ruijie He, Michael Cho, Jiafei Duan
Abstract
General-purpose robot policies must work across robots, tasks, and environments, yet the variation that makes physical trials informative also makes them difficult to reproduce. We introduce RoboColosseum, a distributed evaluation framework that combines unified policy serving and standardized protocols across a network of locally operated robots in diverse environments. Our initial study evaluates five vision-language-action (VLA) policies and three frontier AI agents across four embodiments (SO-101, Franka, Bimanual-YAM, and G1 Humanoid). The Open Track compares models without task-specific adaptation in blinded, pairwise trials under matched local conditions. The Fine-tune Track measures how well VLA policies adapt to 20 tasks across all four embodiments when each policy receives the same 100 demonstrations per task. Across 5,699 pairwise Open Track comparisons at 124 sites, frontier agents rival specialized VLA policies, while all systems struggle to complete open-world tasks reliably. After fine-tuning, GR00T N1.7 leads on SO-101 and pi0.5 leads on Franka and Bimanual-YAM. Policy rankings show weak agreement across embodiment-specific task suites (Kendall W = 0.24). Open Track rankings poorly predict relative performance after fine-tuning, with substantial rank reversals on all three embodiments shared between tracks. Measuring partial progress reveals differences that task success alone misses. On the G1 Humanoid, no policy completes any of the five tasks, yet average progress ranges from 13.3% to 23.2% across policies. Open Track rankings stabilize as trials accumulate, and active matchmaking achieves comparable ranking agreement with substantially fewer physical trials than uniform sampling. These results show how distributed physical evaluation can reveal where robot policies generalize, how well they adapt to new tasks, and how far they progress on tasks they fail to complete.