The Regretful Agent: Heuristic-Aided Navigation through Progress Estimation (1903.01602v1)

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 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. Our code is available at https://github.com/chihyaoma/regretful-agent .

Overview of the "The Regretful Agent: Heuristic-Aided Navigation through Progress Estimation" Paper

This paper addresses the Vision-and-Language Navigation (VLN) task, where an agent must navigate to a goal using language instructions and visual inputs without explicit knowledge of the goal. The research introduces a novel approach that integrates heuristic-aided navigation strategies—specifically through a mechanism referred to as "the regretful agent."

The core contribution of the paper is two-fold: the introduction of a Regret Module and a Progress Marker within an end-to-end trainable architecture. These components are designed to enhance navigation performance by improving decision-making in diverse environments.

Key Components:

1. Regret Module: The Regret Module is a learned mechanism that determines when the agent should backtrack. Leveraging outputs from a progress monitor as a learned heuristic, the module decides whether progressing further or reverting to a prior state aligns better with achieving navigation goals.
2. Progress Marker: This component helps the agent recall visited locations and their contextual relevance via progress estimation. It encodes past navigation decisions, allowing the agent to favor potentially fruitful unexplored paths while avoiding revisiting low-yield locations unless recalibration of the path suggests otherwise.

The integration of these elements significantly improves upon previous methods that relied on beam search, which is computationally expensive and less practical for real-time applications such as robotics.

Performance Evaluation:

The proposed method outperforms existing state-of-the-art published methods on the VLN task, achieving notable improvements in success rate (SR) and success rate weighted by path length (SPL). The introduction of the Regret Module and Progress Marker provided an impressive 8% improvement in SPL on test benchmarks when compared to best-performing existing approaches without beam search. This underscores the practical advantage of the regretful approach in tasks requiring efficient and effective navigation.

Implications and Future Directions:

The strong quantitative results imply that integrating learned heuristics and backtracking strategies in AI navigation systems can close performance gaps in realistic environments where exhaustive search methods like beam search are untenable. The regretful agent's framework could inspire future research into hybrid approaches that fuse decision-making with heuristics tailored through learning.

Looking forward, this work opens pathways for further studies into integrating additional aspects of intelligent search strategies into agents' navigation capabilities, optimally balancing between exploration and exploitation in unfamiliar or complex environments. Further exploration may involve extending this framework to other domains like embodied question answering, where navigating unstructured environments becomes crucial.

This paper’s contributions and findings underscore a methodological advancement in heuristic-aided navigation within AI, representing a promising step towards autonomous systems capable of performing complex tasks through multi-modal inputs and decision-making enhancements.