Informing Multi-Modal Planning with Synergistic Discrete Leads

Z. Kingston, A. M. Wells, M. Moll, and L. E. Kavraki, “Informing Multi-Modal Planning with Synergistic Discrete Leads,” in IEEE Intl. Conf. on Robotics and Automation, 2020.

Abstract

Robotic manipulation problems are inherently continuous, but typically have underlying discrete structure, e.g., whether or not an object is grasped. This means many problems are multi-modal and in particular have a continuous infinity of modes. For example, in a pick-and-place manipulation domain, every grasp and placement of an object is a mode. Usually manipulation problems require the robot to transition into different modes, e.g., going from a mode with an object placed to another mode with the object grasped. To successfully find a manipulation plan, a planner must find a sequence of valid single-mode motions as well as valid transitions between these modes. Many manipulation planners have been proposed to solve tasks with multi-modal structure. However, these methods require mode-specific planners and fail to scale to very cluttered environments or to tasks that require long sequences of transitions. This paper presents a general layered planning approach to multi-modal planning that uses a discrete "lead" to bias search towards useful mode transitions. The difficulty of achieving specific mode transitions is captured online and used to bias search towards more promising sequences of modes. We demonstrate our planner on complex scenes and show that significant performance improvements are tied to both our discrete "lead" and our continuous representation.

PDF preprint: http://kavrakilab.org/publications/kingston2020weighting-multi-modal-leads.pdf