Task-Graph-Guided Demonstration Generation for Multi-Robot Manipulation

Learning visuomotor policies for long-horizon multi-robot manipulation requires demonstrations that capture both reliable object interaction and inter-robot coordination. However, collecting coordinated demonstrations through human teleoperation can be costly and increasingly difficult as the number of robots grows. Script-based demonstrations can reduce reliance on human operators, but they impose synchronization that introduces unnecessary waiting. We present Graph2Demo, a task-graph-guided demonstration-generation framework that combines symbolic planning with residual reinforcement learning. Given a task specification in PDDL, Graph2Demo utilizes a task graph to encode the plan into per-agent subtask sequences and inter-agent dependencies, allowing robots to progress independently and synchronize when needed. To guide subtask execution, the task graph provides target poses for a pretrained pose-reaching base policy and subtask-specific rewards for learning a residual policy to correct the action for object interaction. By combining the base policy and the residual policy, we could generate demonstrations for multi-robot manipulation tasks. Averaged across the 11 tasks from the RoboFactory benchmark, diffusion policies trained on Graph2Demo demonstrations achieve higher success rates than those trained on demonstrations from the baselines, highlighting the potential of task-graph-guided demonstration generation for multi-robot manipulation.