GRAPPA：通过在线智能体引导实现机器人策略的泛化与适配
GRAPPA: Generalizing and Adapting Robot Policies via Online Agentic Guidance
AI总结：
GRAPPA提出一种由多角色对话智能体组成的在线自我引导框架，通过将基础策略接地到环境物体并利用视觉运动线索调整动作分布，在无需额外人类演示的情况下显著提升机器人操作策略在仿真和真实环境中的成功率。
摘要：
行为克隆和强化学习等机器人学习方法已在特定环境中从人类演示合成机器人技能方面展现出巨大前景。然而，这些方法通常需要任务特定的演示或设计复杂的仿真环境，这限制了面向未见真实世界场景的通用且鲁棒策略的开发。近期在机器人领域使用基础模型（如LLM、VLM）的进展显示出巨大潜力，使系统能够从大规模互联网数据中理解世界的语义。然而，如何利用这些知识使机器人系统理解世界的底层动力学、跨不同任务泛化策略以及将策略适配到新环境，仍然是一个开放性挑战。为缓解这些局限性，我们提出了一种用于机器人自我引导和自我改进的智能体框架，该框架由一组角色专用的对话智能体组成，例如高层顾问、接地智能体、监控智能体和机器人智能体。我们的框架迭代地将基础机器人策略接地到环境中的相关物体，并利用视觉运动线索在线将策略的动作分布转移到更理想的状态，同时保持对给定机器人硬件平台的主观配置无关。我们证明，我们的方法能够有效引导操作策略在仿真和真实世界实验中取得显著更高的成功率，而无需额外的人类演示或大量探索。代码和视频可在 https://agenticrobots.github.io 获取。
英文摘要：
Robot learning approaches such as behavior cloning and reinforcement learning have shown great promise in synthesizing robot skills from human demonstrations in specific environments. However, these approaches often require task-specific demonstrations or designing complex simulation environments, which limits the development of generalizable and robust policies for unseen real-world settings. Recent advances in the use of foundation models for robotics (e.g., LLMs, VLMs) have shown great potential in enabling systems to understand the semantics in the world from large-scale internet data. However, it remains an open challenge to use this knowledge to enable robotic systems to understand the underlying dynamics of the world, to generalize policies across different tasks, and to adapt policies to new environments. To alleviate these limitations, we propose an agentic framework for robot self-guidance and self-improvement, which consists of a set of role-specialized conversational agents, such as a high-level advisor, a grounding agent, a monitoring agent, and a robotic agent. Our framework iteratively grounds a base robot policy to relevant objects in the environment and uses visuomotor cues to shift the action distribution of the policy to more desirable states, online, while remaining agnostic to the subjective configuration of a given robot hardware platform. We demonstrate that our approach can effectively guide manipulation policies to achieve significantly higher success rates, both in simulation and in real-world experiments, without the need for additional human demonstrations or extensive exploration. Code and videos available at: https://agenticrobots.github.io
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。
