用图结构生成机器人任务策略，提升复杂场景下的自动化可靠性。
GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks
- 构建图结构策略，融合感知、规划与控制节点
- 在仿真中并行测试多图结构，迭代优化成功率
- 在8个新任务上显著超越基线，适合工业级自动化
为使机器人在商业和工业应用中可靠运行，当前代理编程系统能否结合可解释的机器人编程与无模型策略的开放世界适应性？我们聚焦于“变体自动化”（VA）任务——其物体几何与姿态变化远大于固定自动化任务。无模型策略常难以弥合VA任务的可靠性差距，而这类任务必须持续可靠执行。受任务与运动规划（TAMP）及机器人操作系统（ROS）启发，我们提出图作为策略（GaP），一种多智能体编码框架，从模块化开放机器人技能库（MORSL）生成带方向的计算图，包含感知、规划与控制节点。GaP构建内部仿真环境，平行试运行不同图结构的任务实例，迭代优化图结构与参数以提升成功率与吞吐量。在8个新的开放VA任务基准上评估，包括4个仿真与4个真实场景，结果表明GaP显著优于基线。详细信息、代码与数据见：https://graph-robots.github.io/gap
原文摘要 · Abstract (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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。