2026-09-24T11:02:57Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1629542025-10-07T04:14:13Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Lozano-Pérez, Tomás
Kaelbling, Leslie P.
Yang, Ethan
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2025-10-06T17:36:38Z
2025-10-06T17:36:38Z
2025-05
2025-06-23T14:04:33.098Z
https://hdl.handle.net/1721.1/162954
How can we build a robot that operates autonomously in a home environment over long periods of time? A key requirement is the ability to perceive and understand its surroundings, including the objects it will interact with. This thesis investigates how a robot can reconstruct previously unknown objects and integrate them into a physics simulation for planning. We explore two methods for reconstructing the 3D geometry of objects and test their performance in simulation and in real-world experiments. Our results demonstrate that a learned depth model enables 3D reconstruction of unknown objects and their successful integration into simulation environments. Additionally, we investigate methods for estimating an object’s inertial parameters, using its reconstructed mesh and through manipulation.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
Online Acquisition of Simulatable Rigid Object Models
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
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unknown
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<Abstract>How can we build a robot that operates autonomously in a home environment over long periods of time? A key requirement is the ability to perceive and understand its surroundings, including the objects it will interact with. This thesis investigates how a robot can reconstruct previously unknown objects and integrate them into a physics simulation for planning. We explore two methods for reconstructing the 3D geometry of objects and test their performance in simulation and in real-world experiments. Our results demonstrate that a learned depth model enables 3D reconstruction of unknown objects and their successful integration into simulation environments. Additionally, we investigate methods for estimating an object’s inertial parameters, using its reconstructed mesh and through manipulation.</Abstract>
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