GaP：一种用于变分自动化任务的图即策略多智能体自学习框架

摘要

为了让机器人在商业和工业应用中可靠运行，能否将近期智能体编码系统的进展，与可解释的机器人编程以及无模型策略的开放世界适应性相结合？我们聚焦于“变分自动化”（VA）——一类在物体几何和姿态上变化幅度大于固定自动化的任务。无模型策略往往难以弥合VA任务的可靠性差距，而这些任务需要在商业和工业应用中持续且可靠地执行。受先前关于任务与运动规划（TAMP）及机器人操作系统（ROS）工作的启发，我们提出了“图即策略”（GaP）——一种多智能体编码框架，它从模块化开放机器人技能库（MORSL）中生成包含感知、规划和控制节点的有向计算图。随后，GaP构建一个内部仿真环境，通过并行排练不同图结构下的任务实例，迭代优化图结构与参数，以提升成功率和吞吐量。在8个新的开放VA任务基准（其中4个为仿真任务，4个为真实世界任务）上的评估表明，GaP能够取得显著优于基线的成功率。详情、代码及数据可在线获取：https://graph-robots.github.io/gap

English

For robots to work reliably in commercial and industrial applications, can recent advances in agentic coding systems combine interpretable robot programming with the open-world adaptability of model-free policies? We focus on "Variational Automation" (VA), a class of tasks that have larger variations in object geometry and pose than fixed automation. Model-free policies often struggle to close the reliability gap for VA tasks, which must be executed persistently and reliably in commercial and industrial applications. Motivated by prior work on Task and Motion Planning (TAMP) and the Robot Operating System (ROS), we introduce Graph-as-Policy (GaP), a multi-agent coding harness that generates directed computation graphs with perception, planning, and control nodes from a Modular Open Robot Skill Library (MORSL). GaP then generates an internal simulation environment to rehearse task instances with different graphs in parallel to iteratively refine the graph structure and parameters to improve success rates and throughput. Evaluation with 8 new open VA task benchmarks, 4 in-simulation and 4 in real-world, suggests that GaP can achieve success rates that significantly outperform baselines. Details, code, and data can be found online: https://graph-robots.github.io/gap