Kai Gao, Rutgers University

Rui Wang, Rutgers University

Abstract

10:00 - 10:20 AM  On Minimizing the Number of Running Buffers for Tabletop Rearrangement
Speaker: Kai Gao, Rutgers University
For tabletop rearrangement problems with overhand grasps, storage space outside the tabletop workspace, or buffers, can temporarily hold objects which greatly facilitates the resolution of a given rearrangement task. This brings forth the natural question of how many running buffers are required so that certain classes of tabletop rearrangement problems are feasible. In this work, we examine the problem for both the labeled (where each object has a specific goal pose) and the unlabeled (where goal poses of objects are interchangeable) settings. On the structural side, we observe that finding the minimum number of running buffers (MRB) can be carried out on a dependency graph abstracted from a problem instance, and show that computing MRB on dependency graphs is NP-hard. We then prove that under both labeled and unlabeled settings, even for uniform cylindrical objects, the number of required running buffers may grow unbounded as the number of objects to be rearranged increases; we further show that the bound for the unlabeled case is tight. On the algorithmic side, we develop highly effective algorithms for finding MRB for both labeled and unlabeled tabletop rearrangement problems, scalable to over a hundred objects under very high object density. Employing these algorithms, empirical evaluations show that random labeled and unlabeled instances, which more closely mimics real-world setups, have much smaller MRBs.

This is joint work with Si Wei Feng and Jingjin Yu.

Bio:  Kai Gao is a second-year doctoral student in Robotics at Rutgers, the State University of New Jersey, working with Professor Jingjin Yu. Currently, his research focuses on resolving combinatorial challenges in robot tasks and motion planning. Before arriving at Rutgers, he received a Bachelor's degree in Mathematics from the University of Science and Technology of China in 2019. Email: [email protected]

10:20  - 10:30 AM   Q&A

10:30  - 10:50 AM Planning with Perception in the Loop: Safe and Effective Picking Path in Clutter given Discrete Distributions of Object Poses

Speaker: Rui Wang, Rutgers University

Picking an item in the presence of other objects can be challenging as it involves occlusions and partial views. Given object models, one approach is to perform object pose estimation and use the most likely candidate pose per object to pick the target without collisions. This approach, however, ignores the uncertainty of the perception process both regarding the target's and the surrounding objects' poses. This work proposes first a perception process for 6D pose estimation, which returns a discrete distribution of object poses in a scene. Then, an open-loop planning pipeline is proposed to return safe and effective solutions for moving a robotic arm to pick, which (a) minimizes the probability of collision with the obstructing objects; and (b) maximizes the probability of reaching the target item. The planning framework models the challenge as a stochastic variant of the Minimum Constraint Removal (MCR) problem. The effectiveness of the methodology is verified given both simulated and real data in different scenarios. The experiments demonstrate the importance of considering the uncertainty of the perception process in terms of safe execution. The results also show that the methodology is more effective than conservative MCR approaches, which avoid all possible object poses regardless of the reported uncertainty.

Bio: Rui Wang is a Ph.D. candidate in the department of Computer Science at Rutgers University, supervised by Professor Kostas Bekris. His research lies in task and motion planning on robot manipulation, specifically with failure-explanation planning approaches which reason about the failure of finding a valid plan and use the explanation for further guidance. Prior to his Ph.D. in Rutgers, he received his Master degree in Mechanical Engineering from Columbia University and his Bachelor degree in Vehicle Engineering from Nanjing University of Aeronautics and Astronautics, China. 

10:50 - 11:00 AM  Q&A

SPECIAL NOTE: This seminar is presented online only.

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Meeting number (access code): 120 083 8964

Meeting password: 1234

Presented in association with the DATA-INSPIRE TRIPODS Institute.