Learning to Terminate in Object Navigation
Yuhang Song Anh Nguyen Chun-Yi Lee
This paper tackles the critical challenge of object navigation in autonomous
navigation systems, particularly focusing on the problem of target approach and
episode termination in environments with long optimal episode length in Deep
Reinforcement Learning (DRL) based methods. While effective in environment
exploration and object localization, conventional DRL methods often struggle
with optimal path planning and termination recognition due to a lack of depth
information. To overcome these limitations, we propose a novel approach, namely
the Depth-Inference Termination Agent (DITA), which incorporates a supervised
model called the Judge Model to implicitly infer object-wise depth and decide
termination jointly with reinforcement learning. We train our judge model along
with reinforcement learning in parallel and supervise the former efficiently by
reward signal. Our evaluation shows the method is demonstrating superior
performance, we achieve a 9.3% gain on success rate than our baseline method
across all room types and gain 51.2% improvements on long episodes environment
while maintaining slightly better Success Weighted by Path Length (SPL). Code
and resources, visualization are available at:
https://github.com/HuskyKingdom/DITA_acml2023