用代码模型直接生成机器人可执行动作，无需训练即可适应新环境和物体。
EmbodiedCoder: Parameterized Embodied Mobile Manipulation via Modern Coding Model
- 基于代码模型生成可执行的机器人轨迹，实现端到端控制。
- 在真实机器人上完成多样化长期任务，对新物体和环境泛化能力强。
- 无需额外数据收集或微调，方法透明且可解释，适合复杂场景应用。
近期控制机器人方法，如端到端视觉-语言-动作框架和基于预定义原语的模块化系统，已显著提升机器人理解自然语言指令的能力。然而，许多方法仍难以扩展至多样化环境，因依赖大量标注数据且可解释性有限。本文提出EmbodiedCoder，一种无需训练的开放世界移动操作框架，利用代码模型直接生成可执行的机器人轨迹。通过将高层指令与代码结合，EmbodiedCoder实现了灵活的物体几何参数化和操作轨迹合成，无需额外数据采集或微调。该编码范式为感知与操作提供了透明且通用的连接方式。真实移动机器人实验表明，EmbodiedCoder在多样化长期任务中表现稳健，并能有效泛化至新物体与新环境。结果验证了一种可解释的方法，实现高层推理与底层控制的衔接，推动机器人智能从固定原语迈向多功能适应。
原文摘要 · Abstract (English)
Recent advances in control robot methods, from end-to-end vision-language-action frameworks to modular systems with predefined primitives, have advanced robots' ability to follow natural language instructions. Nonetheless, many approaches still struggle to scale to diverse environments, as they often rely on large annotated datasets and offer limited interpretability.In this work, we introduce EmbodiedCoder, a training-free framework for open-world mobile robot manipulation that leverages coding models to directly generate executable robot trajectories. By grounding high-level instructions in code, EmbodiedCoder enables flexible object geometry parameterization and manipulation trajectory synthesis without additional data collection or fine-tuning.This coding-based paradigm provides a transparent and generalizable way to connect perception with manipulation. Experiments on real mobile robots show that EmbodiedCoder achieves robust performance across diverse long-term tasks and generalizes effectively to novel objects and environments.Our results demonstrate an interpretable approach for bridging high-level reasoning and low-level control, moving beyond fixed primitives toward versatile robot intelligence. See the project page at: https://embodiedcoder.github.io/EmbodiedCoder/
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