TY - GEN
T1 - Object Goal Navigation using Data Regularized Q-Learning
AU - Gireesh, Nandiraju
AU - Sasi Kiran, D. A.
AU - Banerjee, Snehasis
AU - Sridharan, Mohan
AU - Bhowmick, Brojeshwar
AU - Krishna, Madhava
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022/10/28
Y1 - 2022/10/28
N2 - 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.
AB - 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.
KW - Data Augmentation
KW - Deep Reinforcement Learning
KW - Object Goal Navigation
KW - Q-value Regularization
U2 - 10.1109/CASE49997.2022.9926452
DO - 10.1109/CASE49997.2022.9926452
M3 - Conference contribution
AN - SCOPUS:85141743556
T3 - IEEE International Conference on Automation Science and Engineering
SP - 1092
EP - 1097
BT - 2022 IEEE 18th International Conference on Automation Science and Engineering, CASE 2022
PB - IEEE Computer Society Press
T2 - 18th IEEE International Conference on Automation Science and Engineering, CASE 2022
Y2 - 20 August 2022 through 24 August 2022
ER -