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 RoboColos
RoboColosseum evaluates general robot policies across diverse embodiments and environments, revealing weak cross-embodiment agreement, limited open-world reliability, and substantial differences between zero-shot and fine-tuned performance.
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…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
RoboColosseum: Evaluating Physical Intelligence at Scale In-The-Wild was first seen on Hugging Face · Daily papers on Nov 4, 2026. Since then it has appeared in 10 checks on 1 different day. Its best position was #7 of 30, reached on Nov 4, 11:28 UTC. It dropped off the list after Nov 4, 21:28 UTC.
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#7 on Hugging Face · Daily papers, on Nov 4, 11:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 4, 2026 and has been on the list on 1 day (10 checks) since.