CLAS: Central Latent Action Spaces for Coordinated Multi-Robot Manipulation

Elie Aljalbout, Maximilian Karl, Patrick van der Smagt

Keywords: Latent Action Spaces, Multi-Robot Manipulation, Cooperative Control, Reinforcement Learning

TL;DR: We propose a method for coordinating multi-robot manipulation by learning a latent action space that is task specific and acts on the manipulated object.

Abstract: 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 combinatorial explosion 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, learning performance, and interpretability.

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