TY - GEN
T1 - Fast High-Quality Tabletop Rearrangement in Bounded Workspace
AU - Gao, Kai
AU - Lau, Darren
AU - Huang, Baichuan
AU - Bekris, Kostas E.
AU - Yu, Jingjin
N1 - Funding Information:
1Department of Computer Science, Rutgers University, NJ, USA. Email: {kg627, bh417, kb572, jy512}@cs.rutgers.edu. 2Department of Computer Science, Cornell University, NY, USA. Email: {dl755}@cornell.edu. This work is supported by NSF awards IIS-1845888, CCF-1934924 and IIS-2132972.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this paper, we examine the problem of rearranging many objects on a tabletop in a cluttered setting using overhand grasps. Efficient solutions for the problem, which capture a common task that we solve on a daily basis, are essential in enabling truly intelligent robotic manipulation. In a given instance, objects may need to be placed at temporary positions ('buffers') to complete the rearrangement, but allocating these buffer locations can be highly challenging in a cluttered environment. To tackle the challenge, a two-step baseline planner is first developed, which generates a primitive plan based on inherent combinatorial constraints induced by start and goal poses of the objects and then selects buffer locations assisted by the primitive plan. We then employ the 'lazy' planner in a tree search framework which is further sped up by adapting a novel preprocessing routine. Simulation experiments show our methods can quickly generate high-quality solutions and are more robust in solving large-scale instances than existing state-of-the-art approaches. source: github.com/arc-l/TRLB
AB - In this paper, we examine the problem of rearranging many objects on a tabletop in a cluttered setting using overhand grasps. Efficient solutions for the problem, which capture a common task that we solve on a daily basis, are essential in enabling truly intelligent robotic manipulation. In a given instance, objects may need to be placed at temporary positions ('buffers') to complete the rearrangement, but allocating these buffer locations can be highly challenging in a cluttered environment. To tackle the challenge, a two-step baseline planner is first developed, which generates a primitive plan based on inherent combinatorial constraints induced by start and goal poses of the objects and then selects buffer locations assisted by the primitive plan. We then employ the 'lazy' planner in a tree search framework which is further sped up by adapting a novel preprocessing routine. Simulation experiments show our methods can quickly generate high-quality solutions and are more robust in solving large-scale instances than existing state-of-the-art approaches. source: github.com/arc-l/TRLB
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U2 - 10.1109/ICRA46639.2022.9812367
DO - 10.1109/ICRA46639.2022.9812367
M3 - Conference contribution
AN - SCOPUS:85131720887
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 1961
EP - 1967
BT - 2022 IEEE International Conference on Robotics and Automation, ICRA 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th IEEE International Conference on Robotics and Automation, ICRA 2022
Y2 - 23 May 2022 through 27 May 2022
ER -