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| title: README | |
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| <p align="center"> | |
| <a href="https://robocolosseum.ai"> | |
| <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/64b90c96008ff25caf438c1d/_caDZ5p4kJC3UDj2wHHj-.png" width="64" alt="RoboColosseum logo" /> | |
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| <h1 align="center">RoboColosseum</h1> | |
| <p align="center"><strong>The proving ground for physical AI.</strong></p> | |
| <p align="center"> | |
| <a href="https://huggingface.co/RoboColosseum#datasets"><strong>🤗 Explore rollout data</strong></a> | |
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| <a href="https://robocolosseum.ai/leaderboard">🏆 Leaderboard</a> | |
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| <a href="https://robocolosseum.ai">🌐 Website</a> | |
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| <a href="https://arxiv.org/abs/2611.02497">📄 Paper</a> | |
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| <a href="https://github.com/RoboColosseum">💻 GitHub</a> | |
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| **RoboColosseum evaluates robot policies in the real world, at scale.** We measure how reliably models work across everyday environments, tasks, and robot embodiments. | |
| - 🤖 **100+ robots**, including SO-101, Franka, bimanual YAM, and Unitree G1 | |
| - 🌍 **140+ real-world scenes** with thousands of tasks | |
| - 🎓 **10,000 free evaluation hours** in a shared pool for academic labs | |
| ## 🤗 Open rollout data | |
| **Our commitment: every evaluation rollout, openly released on Hugging Face—failures included.** | |
| Explore robot attempts in the [datasets below](https://huggingface.co/RoboColosseum#datasets) to understand policy behavior and failures. Each dataset card explains its contents, evaluation setup, and usage details. | |
| ## 🛤️ Two evaluation tracks | |
| **Open track.** Blind, head-to-head tests measure how well models handle new tasks in real-world environments, with no task-specific training. | |
| **Fine-tune track.** Models learn from the same fixed, open-source dataset, then undergo evaluation to measure how well they adapt to the task. | |
| ## 🎓 Free evaluations for academia | |
| Need evaluations beyond what your lab can run? [Apply for a share of 10,000 free evaluation hours →](https://app.youform.com/forms/riubycrq) | |