Vision-and-Dialog Navigation (1907.04957v3)

Abstract: Robots navigating in human environments should use language to ask for assistance and be able to understand human responses. To study this challenge, we introduce Cooperative Vision-and-Dialog Navigation, a dataset of over 2k embodied, human-human dialogs situated in simulated, photorealistic home environments. The Navigator asks questions to their partner, the Oracle, who has privileged access to the best next steps the Navigator should take according to a shortest path planner. To train agents that search an environment for a goal location, we define the Navigation from Dialog History task. An agent, given a target object and a dialog history between humans cooperating to find that object, must infer navigation actions towards the goal in unexplored environments. We establish an initial, multi-modal sequence-to-sequence model and demonstrate that looking farther back in the dialog history improves performance. Sourcecode and a live interface demo can be found at https://cvdn.dev/

Vision-and-Dialog Navigation

Introduction

The paper "Vision-and-Dialog Navigation," authored by Jesse Thomason, Michael Murray, Maya Cakmak, and Luke Zettlemoyer, explores enhancing robotic navigation systems using natural language dialog. This research proposes Cooperative Vision-and-Dialog Navigation (CVDN), a novel dataset crucial for studying the interplay between vision and dialog in navigation tasks. This dataset is characterized by over 2,000 human-human dialogs, situated within photorealistic, simulated home environments. The core idea is to enable robots to navigate human environments by asking for assistance and comprehending human responses effectively.

Dataset and Task Definition

The CVDN dataset introduces numerous challenges for navigation agents, including ambiguous and underspecified instructions, necessitating effective dialog and context understanding. The dataset leverages the Matterport Room-2-Room (R2R) simulation environment and extends the scope of dialogs to cover longer paths and more detailed instructions compared to R2R.

A key contribution of this paper is the Navigation from Dialog History (NDH) task. This task requires an agent to infer navigation steps given a target object and a sequence of human-human dialogs. The evaluation relies on the progress made towards a goal location, which is measured by the reduction in distance from the starting to the ending point along the path taken by the agent.

Experimental Setup

The authors employ a sequence-to-sequence learning model to establish an initial performance baseline for the NDH task. The encoder LSTM handles the entire dialog history, while the decoder LSTM processes visual frames from the environment to predict navigation actions. The model’s inputs are token embeddings for the dialog history and image embeddings from a pre-trained ResNet-152 for the visual context.

Experiments evaluate the impact of varying amounts of dialog history on the agent’s performance. Notably, encoding a longer dialog history is hypothesized to improve the agent's navigation efficacy. Additionally, the paper investigates the benefits of mixed supervision, combining human and planner steps during training.

Results

Quantitative results reveal several crucial insights:

1. Dialog History Utility: The navigation performance significantly improves when longer dialog histories are encoded. In unseen environments, models leveraging full dialog histories achieve statistically significant gains over those using only the latest exchanges or target object information.
2. Supervision Signal Efficacy: The mixed supervision approach consistently outperforms training based solely on human or planner data. This hybrid method combines the exploratory reach of human guidance with the precision of planner data, yielding superior navigation progress towards the goal.
3. Comparison with Baselines: The sequence-to-sequence models that incorporate comprehensive dialog history and mixed supervision outperform both unimodal and non-learning baselines, particularly in unseen environments.

Summary of Findings

The research highlights several strong numerical results, such as the significant progress towards goal locations when using full dialog histories and mixed supervision. Contradictory to common assumptions, the integration of dialog context spanning multiple turns—rather than relying solely on most recent exchanges—proves critical for effective context comprehension and navigation.

Implications and Future Directions

Practical Implications: The CVDN dataset and NDH task can drive advances in robotic assistance in human environments, enhancing the utility of robots in home and office settings. Training agents that can both ask for and provide navigation assistance can bridge existing gaps between static dialog systems and dynamic, manipulation-capable robots.

Theoretical Implications: This work lays the groundwork for future exploration into cooperative dialog systems, reinforcing theories around language grounding in visual contexts. Additionally, the findings suggest that mixed supervision strategies can robustly improve navigation capabilities, an insight that could extend to other domains of human-robot interaction.

Future Developments: Building on this research, future efforts could focus on refining RL techniques to better leverage human demonstrations, incorporating richer environmental data such as depth information, and creating even more realistic training environments. Advanced formulations that jointly align dialog and navigation histories using cross-modal attention mechanisms could further enhance agent capabilities.

Conclusion

The research presented in "Vision-and-Dialog Navigation" significantly advances our understanding of integrating vision and dialog for robotic navigation in human environments. The introduction of the CVDN dataset and the NDH task provides a robust foundation for training and evaluating dialog-enabled navigation agents. This paper’s findings underscore the importance of comprehensive dialog history and mixed supervision in improving navigation performance, setting the stage for future innovations in dialog-based robotic systems.