Kavraki, Lydia E.2021-11-232021-11-232021-122021-11-22December 2Kingston, Zachary. "Toward Efficient and General Multi-Modal Planning." (2021) Diss., Rice University. <a href="https://hdl.handle.net/1911/111679">https://hdl.handle.net/1911/111679</a>.https://hdl.handle.net/1911/111679Complex robotic systems like mobile manipulators and humanoids will soon be employed outside the highly-structured domain of factories, entering cluttered kitchens, warehouses, and even space stations (e.g., NASA's Robonaut 2). For a robot to be effective in these domains, it is essential that the robot be able to manipulate and navigate the environment to achieve desired tasks, e.g., setting a table and fetching an object on the other side of the space station. These problems can be formulated as multi-modal planning problems, that is, there are a finite number of interactions the robot can have with the environment (e.g., picking up an object, grasping a handrail) each of which with a continuous infinity of possibilities (e.g., where the object is grasped or placed). In this work, we propose an efficient and general solver for these multi-modal problems by tackling three key issues. First, we address the challenges in efficiently solving the arbitrary manifold-constrained motion planning problems that arise in multi-modal planning with a framework that maintains theoretical guarantees of sampling-based planners. Second, we improve planner efficiency with an experience-based framework that learns from prior motion plans, leveraging the structure of multi-modal problems. Finally, we propose a guiding heuristic for multi-modal planners that generates a "lead" of specific actions to attempt, enabling efficient solutions to complex, long-horizon problems.application/pdfengCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.roboticsmotion planningThesis2021-11-23