CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spaces
This addresses coordination challenges in multi-robot systems, offering incremental improvements for robotics applications.
The paper tackles the problem of multi-robot manipulation by proposing a method to coordinate agents through learned latent action spaces, demonstrating improved sample efficiency and learning performance in simulated tasks.
Multi-robot manipulation tasks involve various control entities that can be separated into dynamically independent parts. A typical example of such real-world tasks is dual-arm manipulation. Learning to naively solve such tasks with reinforcement learning is often unfeasible due to the sample complexity and exploration requirements growing with the dimensionality of the action and state spaces. Instead, we would like to handle such environments as multi-agent systems and have several agents control parts of the whole. However, decentralizing the generation of actions requires coordination across agents through a channel limited to information central to the task. This paper proposes an approach to coordinating multi-robot manipulation through learned latent action spaces that are shared across different agents. We validate our method in simulated multi-robot manipulation tasks and demonstrate improvement over previous baselines in terms of sample efficiency and learning performance.