Learning a Decentralized Multi-arm Motion Planner

Many complex manipulation tasks can be decomposed into smaller sub-tasks and distributed amongst multiple robotic arms working in parallel in a shared workspace. However, efficiently motion planning for such multi-arm systems remains a challenge due to its high degrees-of-freedom (DoF) and tightly coupled workspaces. While traditional centralized motion planner [1, 2, 3, 4, 5, 6] benefit from having access to all the information a motion planner module might need, these approaches fail to scale efficiently with the number of arms in the system (the team size) because their centralized components can become the bottleneck of the system. This scalability issue has limited multi-arm applications requiring large numbers of robotic arms operating in a tight workspace or in dynamic environments with moving targets. Less explored alternatives are decentralized motion planners, which treat the multi-arm system as a multi-agent system. Here, each arm is controlled by an agent that receives as input a partial observation of the system's state and computes a motion plan for only itself. Naturally, decentralized motion planners scale efficiently, but designing such a controller for a task as complex as generic multi-arm motion planning remains a challenge. Through observations of other arms' states alone, decentralized motion planners must efficiently coordinate to avoid collisions and cooperate to collectively reach their target end-effector poses, all the while having control over only its arm (Figure 1). An ideal candidate for a multi-arm motion planner should have the following characteristics: - Scalability: The runtime should scale efficiently with the number of arms in the system.