Following High-level Navigation Instructions on a Simulated Quadcopter with Imitation Learning
TABLE II: Development results. We compare our approach against ablations of the different auxiliary objectives and a model that observes noisy poses GSMN+ ηpos.
['[EMPTY]', 'Success Rate (%) Overall', 'Success Rate (%) Landmark', 'Dst. to Goal (m) Mean', 'Dst. to Goal (m) Median']
We ablate each auxiliary objective. Each objective contributes to the model performance. We observe that the grounding objective Jground is essential and without it the model fails to learn. The planning auxiliary objective Jplan is the least important. This suggests that after having successful grounding results, the set of simple spatial relations we use is relatively easy to learn. We also observe that our model is robust to moderate amount of localization noise without a significant decline in performance. This indicates the model potential for physical robotic systems, where position estimates are likely to be noisy.