UniManip: 通用目的零样本机器人操作的代理操作图
UniManip: General-Purpose Zero-Shot Robotic Manipulation with Agentic Operational Graph
AI总结：
UniManip通过双层代理操作图实现通用零样本机器人操作，结合高层任务协调与底层动态状态表示，提升无监督环境下的执行鲁棒性。
摘要：
实现通用目的机器人操作需要机器人能够无缝地在无结构环境中将高层语义意图与底层物理交互联系起来。然而，现有方法在零样本泛化方面存在不足：端到端的视觉-语言-动作（VLA）模型往往缺乏长时间任务所需的精度，而传统分层规划器在面对开放世界变化时表现出语义刚性。为此，我们提出了UniManip，一种基于双层代理操作图（AOG）的框架，该框架统一了语义推理和物理基础。通过将高层代理层用于任务协调与底层场景层用于动态状态表示相结合，系统能够持续地将抽象规划与几何约束对齐，从而实现稳健的零样本执行。与静态管道不同，UniManip操作为动态代理循环：它主动从无结构感知中实例化以对象为中心的场景图，通过安全意识的局部规划器将这些表示参数化为无碰撞轨迹，并利用结构化记忆自主诊断和恢复执行失败。广泛的实验验证了系统在未见过的对象和任务上的稳健零样本能力，显示出比最先进的VLA和分层基线分别高出22.5%和25.0%的成功率。值得注意的是，系统能够直接从固定基底设置转移到移动操作，而无需微调或重新配置。我们的开源项目页面可在https://henryhcliu.github.io/unimanip上找到。
英文摘要：
Achieving general-purpose robotic manipulation requires robots to seamlessly bridge high-level semantic intent with low-level physical interaction in unstructured environments. However, existing approaches falter in zero-shot generalization: end-to-end Vision-Language-Action (VLA) models often lack the precision required for long-horizon tasks, while traditional hierarchical planners suffer from semantic rigidity when facing open-world variations. To address this, we present UniManip, a framework grounded in a Bi-level Agentic Operational Graph (AOG) that unifies semantic reasoning and physical grounding. By coupling a high-level Agentic Layer for task orchestration with a low-level Scene Layer for dynamic state representation, the system continuously aligns abstract planning with geometric constraints, enabling robust zero-shot execution. Unlike static pipelines, UniManip operates as a dynamic agentic loop: it actively instantiates object-centric scene graphs from unstructured perception, parameterizes these representations into collision-free trajectories via a safety-aware local planner, and exploits structured memory to autonomously diagnose and recover from execution failures. Extensive experiments validate the system's robust zero-shot capability on unseen objects and tasks, demonstrating a 22.5% and 25.0% higher success rate compared to state-of-the-art VLA and hierarchical baselines, respectively. Notably, the system enables direct zero-shot transfer from fixed-base setups to mobile manipulation without fine-tuning or reconfiguration. Our open-source project page can be found at https://henryhcliu.github.io/unimanip.
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。
