Today's off-the-shelf LMs have incredible generalization, reasoning, and planning capabilities. Agentic harnesses in CaP-Agent0 unleashes their potential in the physical world.
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CaP-Bench provides the first comprehensive benchmark for evaluating how well large language model agents can write code to control robots. Integrated with hundreds of manipulation tasks across multiple robot learning benchmarks (LIBERO-PRO, Robosuite, BEHAVIOR), CaP-Bench tests both LLM and VLM models on their ability to generate executable robot control policies from natural language instructions.
Click on each task to see the agent in action.