CaP-X: 用于基准测试和改进机器人操作编码智能体的框架
CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation
AI总结：
提出CaP-X框架，通过CaP-Gym交互环境和CaP-Bench评估，发现编码智能体依赖人工抽象，但通过扩展测试时计算可提升鲁棒性，并推出无需训练的CaP-Agent0和强化学习CaP-RL。
摘要：
“代码即策略”考虑了可执行代码如何补充数据密集型的视觉-语言-动作（VLA）方法，但它们作为具身操作的自主控制器的有效性仍未得到充分探索。我们提出了CaP-X，一个用于系统研究机器人操作中代码即策略智能体的开放访问框架。其核心是CaP-Gym，一个交互环境，智能体通过合成和执行组合感知与控制原语的程序来控制机器人。在此基础上，CaP-Bench评估了前沿语言和视觉-语言模型在不同抽象层次、交互和感知基础下的表现。在12个模型上，CaP-Bench揭示了一个一致的趋势：性能随着人工构建的抽象而提高，但随着这些先验知识的移除而下降，暴露了对设计者支架的依赖。同时，我们观察到这种差距可以通过扩展智能体测试时计算来缓解——通过多轮交互、结构化执行反馈、视觉差异、自动技能合成和集成推理，即使智能体在低级原语上操作，也能显著提高鲁棒性。这些发现使我们能够推导出CaP-Agent0，一个无需训练的框架，在模拟和真实实体上的多个操作任务中恢复了人类水平的可靠性。我们进一步引入了CaP-RL，展示了具有可验证奖励的强化学习提高了成功率，并以最小的差距从模拟转移到真实。总之，CaP-X为推进具身编码智能体提供了一个原则性的、开放访问的平台。
英文摘要：
"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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
