CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation

Code-as-Policy (CaP) is a paradigm in which a language or vision-language model generates executable robot control programs, yet its effectiveness as an autonomous controller for embodied manipulation remains underexplored. Prior CaP systems often rely on high-level, human-designed primitives, making it difficult to separate agent capability from designer-provided scaffolding. We present CaP-X, an open-access framework for systematically studying Code-as-Policy agents in robot manipulation. CaP-X includes four components. CaP-Gym is an interactive environment in which coding agents control robots by synthesizing and executing programs that compose perception and control primitives. Building on this foundation, CaP-Bench evaluates frontier language and vision-language models across varying levels of abstraction, interaction, and perceptual grounding. Across 12 models, the task success rates improve with human-crafted abstractions but degrade as these priors are removed, exposing a dependence on designer scaffolding. At the same time, we observe that scaling test-time computation with multi-turn interaction, structured execution feedback, visual differencing, automatic skill synthesis, and ensembled reasoning can substantially improve robustness even when agents operate over low-level primitives. These findings motivate CaP-Agent0, a training-free framework that achieves near human-level reliability on several manipulation tasks in simulation and on real embodiments. CaP-RL explores reinforcement learning with verifiable rewards to improve success rates and supports sim-to-real transfer through a shared code-as-action-space interface. Together, CaP-X provides an open-access platform for advancing embodied coding agents. Project page: https://cap-x-anonymous.github.io/