Introducing SimInfinity -- the world's largest open-source robotic simulation benchmark
Coming soon: 10K open-source simulations with well-defined robotic tasks inspired by real-world activities across factories, logistics, homes, and beyond.
Verbal reinforcement learning is very effective for improving agentic performance on benchmarks, but we wanted to test how this transfers to robotics. We ran GPT 6 Astra, Gemini Argon 4, and Fable 5.1 on the LIBERO benchmark, and then iteratively developed skills which significantly improved performance.
Full results linked in the comments
Frontier models like GPT-6 Astra and Gemini Argon 4 can control robots, but how much does the harness around them impact performance?
We used our Nomad-Harness to test Gemini Argon on LIBERO tasks to test if text based skills could help with harder tasks that require more manipulation. We iteratively refined the skill with a critic agent reviewing failed attempts.
Across 20 initial states per task, Argon’s native success rate improved:
• Bowl into drawer: 0/20 → 17/20
• Mug into microwave: 0/20 → 8/20
These results show that reusable text instructions can improve robot control, and they also transfer between models, with improvements also shown for GPT-6-Astra and Fable 5.1 with the same skill.
🚀 Runtime (YC F26): The simulation and evaluation platform for physical AI Build realistic environments, stress-test robot policies across thousands of variations, and identify failures before real-world deployment. Building robots? We'd love to chat!
🚀 Runtime (YC F26): The simulation and evaluation platform for physical AI
Build realistic environments, stress-test robot policies across thousands of variations, and identify failures before real-world deployment.
Building robots? We'd love to chat! Reach out to us at https://t.co/AAZNT7qaUO