Past Events

PhD Defense

Complete and Efficient Prehensile Rearrangement in Confined Spaces under Kinematic Constraints

 

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Friday, October 21, 2022, 05:00pm - 07:00pm

 

Speaker: Rui Wang

Location : Room 203, 1 Spring Street, New Brunswick, 08901

Committee

Professor Kostas Bekris (Chair)
Professor Jingjin Yu
Professor Fred Roberts
Professor Siddharth Srivastava (Arizona State University)

Event Type: PhD Defense

Abstract: Rearranging objects in confined spaces has broad applications, such as rearranging products in grocery shelves (e.g., restocking), retrieving food from a packed refrigerator and assembling a product out of individual components. Meanwhile solving such problems in confined spaces is challenging as no top-down grasps are available for approaching the objects, which simplify rearrangement tasks on tabletops. As a result, these problems involve challenging kinematic and geometric constraints, which include both robot-to-object and object-to-object interactions.This thesis is motivated by this domain and proposes task and motion planning algorithms that result in a high-quality sequence of robot motions, which allow to successfully complete rearrangement tasks in confined spaces without undesirable collisions. Specifically, this work introduces efficient monotone solvers for solving monotone problems, i.e., those that can be solved by moving each object at most once, by significantly pruning the search space of possible solutions. In this process, it sidesteps expensive computational operations, while maintaining desirable completeness guarantees. This thesis progresses in incorporating the proposed monotone solvers to develop probabilistically complete non-monotone solvers, which are capable of solving harder instances quickly with fewer buffer locations, i.e., intermediate placements for objects needed during the rearrangement process. This work also provides improved motion planning primitives for rearrangement to speed up online motion planning resolution. The combination of these algorithmic improvements allows for increased feasibility, efficiency and quality of solutions. Finally, this work demonstrates the applicability of the proposed methods via a proof-of-concept real robotic rearrangement system, which integrates visual input and the developed task and motion planning methods.

Contact  Rui Wang

Zoom Meeting: https://rutgers.zoom.us/j/93921947242?pwd=cC95SVYrbGo3SlpQeXJKazdtU2lodz09