论文标题
CLAS：与中央潜在作用空间协调多机器人操纵
CLAS: Coordinating Multi-Robot Manipulation with Central Latent Action Spaces
论文作者
论文摘要
多机器人操纵任务涉及可以将动态独立部分分为动态独立部分的各种控制实体。这种现实世界任务的一个典型例子是双臂操纵。由于样本的复杂性和探索要求随动作和状态空间的维度而增长，因此学习通过加强学习来天真地解决此类任务通常是不可行的。取而代之的是，我们想处理诸如多代理系统之类的环境，并让几个代理控制整体的部分。但是，分散行动的产生需要通过仅限于任务中心的信息的渠道进行协调。本文提出了一种通过在不同代理之间共享的潜在行动空间来协调多机器人操作的方法。我们在模拟多机器人操纵任务中验证了我们的方法，并在样本效率和学习绩效方面证明了对先前基线的改善。
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.
