CaPtain: A Pipeline for Post-Training Code-as-Policy Robot Agents
Overview
This paper introduces CaPtain, an open pipeline that converts a small open vision-language model into a capable code-as-policy robot agent. The method uses supervised fine-tuning on synthetic trajectories and multi-turn reinforcement learning with a physical critic to handle delayed credit assignment. Starting from Qwen3.5-4B (0% zero-shot), CaPtain-4B achieves 73.8% success on the LIBERO benchmark after RL, surpassing several frontier-model agents on CaP-Bench and demonstrating sim-to-real transfer on a real robot without target-domain training.
Generated from attributed reports · Updated 2 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · improvement on perturbation suites: 17.6 other · Basis not reported · Differing source assertions
“ins persist across three perturbation suites (+17.6 to +21.2 points). Without target-domain training, it attains 83.3% on SimplerEnv WidowX, exceeds frontier models on CaP-Bench, and runs on a real robot after simulation-only adaptation. We release all code, data, and model weights.”
Exact source · revision 1Source owner not reported
Reported quantity · improvement on perturbation suites: 21.2 other · Basis not reported · Differing source assertions
“ins persist across three perturbation suites (+17.6 to +21.2 points). Without target-domain training, it attains 83.3% on SimplerEnv WidowX, exceeds frontier models on CaP-Bench, and runs on a real robot after simulation-only adaptation. We release all code, data, and model weights.”
Exact source · revision 1Source owner not reported
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalCaPtain: A Pipeline for Post-Training Code-as-Policy Robot Agents
This paper introduces CaPtain, an open pipeline that converts a small open vision-language model into a capable code-as-policy robot agent. The method uses supervised fine-tuning on synthetic trajectories and multi-turn reinforcement learning with a physical critic to handle delayed credit assignment. Starting from Qwen3.5-4B (0% zero-shot), CaPtain-4B achieves 73.8% success on the LIBERO benchmark after RL, surpassing several frontier-model agents on CaP-Bench and demonstrating sim-to-real transfer on a real robot without target-domain training.
Event coverage history
There is not enough continuous observation data to show a trend.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).