Object Goal Navigation using Data Regularized Q-Learning

Nandiraju Gireesh, D. A. Sasi Kiran, Snehasis Banerjee, Mohan Sridharan, Brojeshwar Bhowmick, Madhava Krishna

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Object Goal Navigation requires a robot to find and navigate to an instance of a target object class in a previously unseen environment. Our framework incrementally builds a semantic map of the environment over time, and then repeatedly selects a long-term goal ('where to go') based on the semantic map to locate the target object instance. Long-term goal selection is formulated as a vision-based deep reinforcement learning problem. Specifically, an Encoder Network is trained to extract high-level features from a semantic map and select a long-term goal. In addition, we incorporate data augmentation and Q-function regularization to make the long-term goal selection more effective. We report experimental results using the photo-realistic Gibson benchmark dataset in the AI Habitat 3D simulation environment to demonstrate substantial performance improvement on standard measures in comparison with a state of the art data-driven baseline.

Original languageEnglish
Title of host publication2022 IEEE 18th International Conference on Automation Science and Engineering, CASE 2022
PublisherIEEE Computer Society Press
Pages1092-1097
Number of pages6
ISBN (Electronic)9781665490429
DOIs
Publication statusPublished - 28 Oct 2022
Event18th IEEE International Conference on Automation Science and Engineering, CASE 2022 - Mexico City, Mexico
Duration: 20 Aug 202224 Aug 2022

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2022-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference18th IEEE International Conference on Automation Science and Engineering, CASE 2022
Country/TerritoryMexico
CityMexico City
Period20/08/2224/08/22

Keywords / Materials (for Non-textual outputs)

  • Data Augmentation
  • Deep Reinforcement Learning
  • Object Goal Navigation
  • Q-value Regularization

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