Resilient Object Perception for Robotics

A broad array of applications, ranging from search and rescue to self-driving vehicles, requires robots to perceive and understand the geometry of objects in the environment. Object perception needs to reliably work in a variety of scenarios and preserve a desired level of performance in the face of outliers and shifts from the training domain. Obtaining such a level of performance requires robust estimation algorithms that are able to identify and reject outliers, as well as techniques to continually improve performance of learningbased perception modules during test-time. In this thesis, we address these challenges by proposing (1) certifiably optimal solvers and a graph-theoretic framework that together help achieve state-of-the-art pose estimation performance even under high outlier rates, (2) self-supervised object pose estimators that can improve performance during test-time with accuracy comparable to state-of-the-art supervised methods, and (3) a test-time adaptation method for both object shape reconstruction and pose estimation without the need for CAD models. Throughout the thesis, we demonstrate that by using a variety of tools from optimization and learning, we can develop resilient object perception systems that perform reliably in a wide range of conditions.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Aeronautics and Astronautics

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Shi, Jingnan

Advisor dc:contributor.advisor

• Carlone, Luca

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/1721.1/163020

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/163020