Identifying Objects’ Inertial Parameters with
Robotic Manipulation to Create Simulation-Ready
Assets
Name
lambert-aalamber-meng-eecs-2023-thesis.pdf
Description
Thesis PDF
Size
7.79 MB
Format
Adobe PDF
Checksum (MD5)
215f7060f8c7511c5690c7cff2ba595e
Author(s)
Lambert, Andy
Advisor(s)
Tedrake, Russ
Date Issued
June 2023
Publisher
Massachusetts Institute of Technology
Abstract
Real2Sim is the problem of simulating objects and scenes via real world data, allowing a robot to imagine future interactions with its environment. However, many existing approaches either do not consider the dynamics of objects being simulated or make assumptions about their mass distributions. In this work, we aim to make use of robotic arm payload identification techniques in order to enhance the dynamic accuracy of objects generated from a Real2Sim pipeline for manipulation tasks. While the payload identification literature is vast, applying these methods in practice has various challenges and limitations. Upon implementing these techniques, we gain understanding of best practices in the engineering sense. We hope that these methods can be used to provide ground truth data for other robot learning tasks on the road towards generalized dynamic intuition.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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