Success Classification for Object Navigation

Object navigation is the embodied task of navigating to an instance of a specified object in unseen environments. Previous work has made impressive progress on the problem, but there remains much room for improvement with current state-of-the-art methods reaching a success rate of less than one in three. In this work, we evaluate a state-of-the-art approach, identifying false positives in object detection as the main point of failure. We propose introducing a new module to verify success when the agent attempts to stop. We introduce a learning-based classifier that learns and compares embeddings for visual observations and object categories and find that it works well at predicting success, outperforming both naive baselines and a heuristic­-based classifier. We also find no improvement when using a ensemble model for semantic segmentation, although we believe there is more to be tested before arriving at a conclusive judgement.

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

2022

Author dc:creator

• Yue, Albert

Advisor dc:contributor.advisor

• Agrawal, Pulkit

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

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

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/145058

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/145058