Effective Robot Teammate Behaviors For Supporting Sequential Manipulation Tasks

In this work, we present an algorithm for improving collaborator performance on sequential manipulation tasks. Our agent-decoupled, optimization-based, task and motion planning approach merges considerations derived from both symbolic and geometric planning domains. This results in the generation of supportive behaviors enabling a teammate to reduce cognitive and kinematic burdens during task completion. We describe our algorithm alongside representative use cases, with an evaluation based on solving complex circuit building problems. We conclude with a discussion of applications and extensions to human-robot teaming scenarios.