Purdue University Graduate School
MS_Thesis.pdf (8.61 MB)

Multi-Agent Neural Rearrangement Planning of Objects in Cluttered Environments

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posted on 2023-07-27, 17:26 authored by Vivek GuptaVivek Gupta

Object rearrangement is a fundamental problem in robotics with various practical applications ranging from managing warehouses to cleaning and organizing home kitchens. While existing research has primarily focused on single-agent solutions, real-world scenarios often require multiple robots to work together on rearrangement tasks. We propose a comprehensive learning-based framework for multi-agent object rearrangement planning, addressing the challenges of task sequencing and path planning in complex environments. The proposed method iteratively selects objects, determines their relocation regions, and pairs them with available robots under kinematic feasibility and task reachability for execution to achieve the target arrangement. Our experiments on a diverse range of environments demonstrate the effectiveness and robustness of the proposed framework. Furthermore, results indicate improved performance in terms of traversal time and success rate compared to baseline approaches. The videos and supplementary material are available at https://sites.google.com/view/maner-supplementary


Degree Type

  • Master of Science


  • Computer Science

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Ahmed H. Qureshi

Advisor/Supervisor/Committee co-chair

Aniket Bera

Additional Committee Member 2

Yexiang Xue