这篇论文提出GaP，把机器人编程的可靠性和模型无关策略的适应性结合起来，在8个变分自动化任务上吊打基线。做机器人任务规划的话值得一看。
GaP（Graph-as-Policy）是一种多智能体编码框架，从模块化开放机器人技能库MORSL生成含感知、规划和控制节点的有向计算图。它构建内部仿真环境并行排练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和吞吐量。在8个新开放VA任务基准（4个仿真、4个真实世界）上，GaP的成功率显著优于基线方法。论文、代码和数据已开源。
GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks
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