Multi-Robot Task Acquisition Through Sparse Coordination

In this paper, we consider several autonomous robots with separate tasks that require coordination, but not a coupling at every decision step. We assume that each robot separately acquires its task, possibly from different providers. We address the problem of multiple robots incrementally acquiring tasks that require their sparse-coordination.To this end, we present an approach to provide tasks to multiple robots, represented as sequences, conditionals, and loops of sensing and actuation primitives. Our approach leverages principles from sparse-coordination to acquire and represent these joint-robot plans compactly. Specifically, each primitive has associated preconditions and effects, and robots can condition on the state of one another. Robots share their state externally using a common domain language. The complete sparse-coordination framework runs on several robots. We report on experiments carried out with a Baxter manipulator and a CoBot mobile service robot.