Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout (1904.04195v1)

Abstract: A grand goal in AI is to build a robot that can accurately navigate based on natural language instructions, which requires the agent to perceive the scene, understand and ground language, and act in the real-world environment. One key challenge here is to learn to navigate in new environments that are unseen during training. Most of the existing approaches perform dramatically worse in unseen environments as compared to seen ones. In this paper, we present a generalizable navigational agent. Our agent is trained in two stages. The first stage is training via mixed imitation and reinforcement learning, combining the benefits from both off-policy and on-policy optimization. The second stage is fine-tuning via newly-introduced 'unseen' triplets (environment, path, instruction). To generate these unseen triplets, we propose a simple but effective 'environmental dropout' method to mimic unseen environments, which overcomes the problem of limited seen environment variability. Next, we apply semi-supervised learning (via back-translation) on these dropped-out environments to generate new paths and instructions. Empirically, we show that our agent is substantially better at generalizability when fine-tuned with these triplets, outperforming the state-of-art approaches by a large margin on the private unseen test set of the Room-to-Room task, and achieving the top rank on the leaderboard.

Analysis of "Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout"

The paper by Hao Tan, Licheng Yu, and Mohit Bansal presents a novel approach to developing navigational agents capable of operating in environments not encountered during training. This research addresses a significant AI challenge: enabling robots to execute navigation tasks based on natural language instructions in unpredictable settings. The principal innovation of this work lies in its two-stage training framework, coupled with the introduction of an 'environmental dropout' method to enhance navigational generalizability in unseen environments.

The first training phase utilizes a hybrid approach, combining imitation learning (IL) with reinforcement learning (RL). This mixed strategy takes advantage of both on-policy and off-policy optimization techniques, achieving better performance than applying IL or RL independently. The results indicate that such a combination supports learning from both the predefined optimal actions and dynamic feedback from the environment, leading to a more robust navigational policy.

The secondary training phase introduces an environmental dropout mechanism as a component of semi-supervised learning. The environmental dropout simulates unseen environments by applying view- and viewpoint-consistent transformations to the input visual features, thereby overcoming the limitations of the variability in training environments. This is done alongside back-translation techniques that generate augmented data by synthesizing novel instructions for unexplored routes within these dropped-out environments.

Empirical evaluation on the Room-to-Room (R2R) task provides compelling evidence of the agent's superior generalization capabilities. The model demonstrates a significant performance enhancement over state-of-the-art methods in the unseen environment tests, particularly on the private test set, where it achieves the top rank. This efficacy is quantified by success rate improvements of 9% in single-run setups compared to prior models and increased performance across various assessment conditions, including beam search and pre-exploration settings.

A careful examination of the methodology reveals that the environmental dropout—designed to produce novel visual contexts—substantially enriches the agent's training experiences without the need for laborious data collection in new environments. This dropout technique exhibits marked benefits over traditional feature dropout, which fails to maintain consistency across related visual inputs. Semantic views were analyzed to further understand dropout mechanisms, reinforcing the effectiveness of environmental dropout over feature dropout.

The research highlights the crucial impact of data diversity, particularly the spatial and thematic variability offered by distinct environments, in enhancing the navigational proficiency of agents. By providing a pathway to generate synthetic environments, this work contributes to overcoming practical limitations in dataset collection, thereby fostering advancements in embodied AI.

The implications of this paper extend to practical applications in developing domestic service robots and autonomous systems, where adaptability to unfamiliar settings is paramount. Theoretically, the research underscores the necessity of innovative data augmentation strategies in machine learning. Future explorations might focus on refining environmental dropout techniques and applying this approach to other complex AI tasks involving vision-and-language interactions.

The work reflects an important step towards creating more adaptable and intelligent navigational agents, promoting a deeper understanding of transferring learned skills across diverse contexts in AI systems.