The Regretful Agent: Heuristic-Aided Navigation Through Progress Estimation

Chih-Yao Ma, Zuxuan Wu, Ghassan AlRegib, Caiming Xiong, Zsolt Kira
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
I know I came from there. Where should I go next? My estimated confidence decreased. Something went wrong. Let's learn this lesson and go back. Instruction: Exit the room. Walk past the display case and into the kitchen. Stop by the table. 20% 13% 25% 42% 60% 75% 90% 1 st step 1 st step 2 nd 5 th 5 th step 4 th 6 th 7 th Figure 1 : Vision-and-Language Navigation task and our proposed regretful navigation agent. The agent leverages the selfmonitoring mechanism [13] through time to decide when to
more » ... e to decide when to roll back to a previous location and resume the instructionfollowing task. Our code is available at Abstract As deep learning continues to make progress for challenging perception tasks, there is increased interest in combining vision, language, and decision-making. Specifically, the Vision and Language Navigation (VLN) task involves navigating to a goal purely from language instructions and visual information without explicit knowledge of the goal. Recent successful approaches have made in-roads in achieving good success rates for this task but rely on beam search, which thoroughly explores a large number of trajectories and is unrealistic for applications such as robotics. In this paper, inspired by the intuition of viewing the problem as search on a navigation graph, we propose to use a progress monitor developed in prior work as a learnable heuristic for search. We then propose two modules incorporated into an end-to-end architecture: 1) A learned mechanism to perform backtracking, which decides whether to continue moving forward or roll back to a previous state (Regret Module) and 2) A mechanism to help the agent decide which direction to go next by showing directions that are visited and their associated progress estimate (Progress * Work partially done while the author was a research intern at Salesforce Research. Marker). Combined, the proposed approach significantly outperforms current state-of-the-art methods using greedy action selection, with 5% absolute improvement on the test server in success rates, and more importantly 8% on success rates normalized by the path length.
doi:10.1109/cvpr.2019.00689 dblp:conf/cvpr/MaWAXK19 fatcat:kzntkxo3evempnsqryuvytdaz4