Identifying Objects’ Inertial Parameters with Robotic Manipulation to Create Simulation-Ready Assets

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Lambert, Andy

Advisor dc:contributor.advisor

• Tedrake, Russ

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/151518

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/151518