EmbodiedCoder：基于现代编码模型的参数化具身移动操作
EmbodiedCoder: Parameterized Embodied Mobile Manipulation via Modern Coding Model
AI总结：
EmbodiedCoder提出无训练框架，利用编码模型直接生成可执行轨迹，实现开放世界移动操作的参数化与泛化，实验验证其稳健性。
摘要：
从端到端的视觉-语言-动作框架到具有预定义原语的模块化系统，控制机器人方法的最新进展提升了机器人遵循自然语言指令的能力。尽管如此，许多方法仍难以扩展到多样化的环境，因为它们通常依赖大型标注数据集，且可解释性有限。在这项工作中，我们引入了EmbodiedCoder，一个用于开放世界移动机器人操作的无训练框架，它利用编码模型直接生成可执行的机器人轨迹。通过将高级指令以代码形式落地，EmbodiedCoder实现了灵活的对象几何参数化和操作轨迹合成，无需额外数据收集或微调。这种基于编码的范式提供了一种透明且可泛化的方式，将感知与操作连接起来。在真实移动机器人上的实验表明，EmbodiedCoder在多样化的长期任务中实现了稳健的性能，并能有效泛化到新颖对象和环境。我们的结果展示了一种可解释的方法，用于弥合高级推理与低级控制之间的鸿沟，超越固定原语，迈向多功能的机器人智能。项目页面见：https://embodiedcoder.github.io/EmbodiedCoder/
英文摘要：
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/
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。
