CaP-X: Framework for Benchmarking Coding Agents for Robot Manipulation
What it is
CaP-X is an open-access framework for studying Code-as-Policy agents in robot manipulation, comprising interactive Gymnasium environments (CaP-Gym), a systematic benchmark (CaP-Bench), a training-free agentic framework (CaP-Agent0), and a reinforcement learning module (CaP-RL). It allows agents to control robots by generating Python code that composes perception and control primitives across simulators like Robosuite, LIBERO-PRO, and BEHAVIOR.
The problem it solves
Lack of a unified, systematic framework to benchmark and improve coding agents specifically for robot manipulation tasks, which previously required ad-hoc setups across different simulators and evaluation protocols.
What you could use it for
- Benchmarking LLM coding agents on robot manipulation tasks across different abstraction levels and interaction modes
- Training language models via reinforcement learning (GRPO) to improve code generation for robot control
- Developing training-free agentic workflows with visual differencing and skill libraries for robot tasks
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