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

• URL: http://arxiv.org/abs/2603.22435v1
• Date: Mon, 23 Mar 2026 18:08:10 GMT
• Title: CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation
• Abstract summary: "Code-as-Policy" considers how executable code can complement data-intensive Vision-Language-Action methods.<n>We present CaP-X, an open-access framework for studying Code-as-Policy agents in robot manipulation.
• Score: 48.85772216740915
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: "Code-as-Policy" considers how executable code can complement data-intensive Vision-Language-Action (VLA) methods, yet their effectiveness as autonomous controllers for embodied manipulation remains underexplored. We present CaP-X, an open-access framework for systematically studying Code-as-Policy agents in robot manipulation. At its core is CaP-Gym, an interactive environment in which 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, CaP-Bench reveals a consistent trend: performance improves with human-crafted abstractions but degrades as these priors are removed, exposing a dependence on designer scaffolding. At the same time, we observe that this gap can be mitigated through scaling agentic test-time computation--through multi-turn interaction, structured execution feedback, visual differencing, automatic skill synthesis, and ensembled reasoning--substantially improves robustness even when agents operate over low-level primitives. These findings allow us to derive CaP-Agent0, a training-free framework that recovers human-level reliability on several manipulation tasks in simulation and on real embodiments. We further introduce CaP-RL, showing reinforcement learning with verifiable rewards improves success rates and transfers from sim2real with minimal gap. Together, CaP-X provides a principled, open-access platform for advancing embodied coding agents.

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