Self-Monitoring Navigation Agent via Auxiliary Progress Estimation
Table 1: Performance comparison with the state of arts: Student-forcing (Anderson et al., 2018b), RPA (Wang et al., 2018b), and Speaker-Follower (Fried et al., 2018). *: with data augmentation. leaderboard: when using beam search, we modify our search procedure to comply with the leaderboard guidelines, i.e., all traversed viewpoints are recorded.
['[EMPTY]', 'Validation-Seen', 'Validation-Seen', 'Validation-Seen', 'Validation-Seen', 'Validation-Unseen', 'Validation-Unseen', 'Validation-Unseen', 'Validation-Unseen', 'Test (unseen)', 'Test (unseen)', 'Test (unseen)', 'Test (unseen)']
We first compare the proposed self-monitoring agent with existing approaches. We achieve 70% SR on the seen environment and 57% on the unseen environment while the existing best performing method achieved 63% and 50% SR respectively. We achieve 3% and 8% improvement on SR on both validation and test unseen environments. Both results with or without data augmentation indicate that our proposed approach is more generalizable to unseen environments. At the time of writing, our self-monitoring agent is ranked #1 on the challenge leader-board among the state of the arts. For comparison without using beam search, please refer to the Appendix. Data augmentation. In the above, we have shown each row in our approach contributes to the performance. Each of them increases the success rate and reduces the navigation error incrementally. This demonstrates that our proposed method is more data-efficient.