Waypoint Models for Instruction-guided Navigation in Continuous Environments (2110.02207v1)

Abstract: Little inquiry has explicitly addressed the role of action spaces in language-guided visual navigation -- either in terms of its effect on navigation success or the efficiency with which a robotic agent could execute the resulting trajectory. Building on the recently released VLN-CE setting for instruction following in continuous environments, we develop a class of language-conditioned waypoint prediction networks to examine this question. We vary the expressivity of these models to explore a spectrum between low-level actions and continuous waypoint prediction. We measure task performance and estimated execution time on a profiled LoCoBot robot. We find more expressive models result in simpler, faster to execute trajectories, but lower-level actions can achieve better navigation metrics by approximating shortest paths better. Further, our models outperform prior work in VLN-CE and set a new state-of-the-art on the public leaderboard -- increasing success rate by 4% with our best model on this challenging task.

Overview of Waypoint Models for Instruction-guided Navigation in Continuous Environments

The paper presents a novel approach to instruction-guided visual navigation focused on waypoint prediction in continuous environments. Unlike traditional models trained in discrete settings, this paper investigates the role of action spaces, examining waypoint models' expressivity on navigation success and efficiency. The work is contextualized within the VLN-CE task, leveraging continuous simulated environments to enable free movement and examining the interaction between high-level waypoint prediction and low-level navigation controls.

Key Findings

The research proposes a class of language-conditioned waypoint prediction networks designed to analyze the trade-offs between expressivity and performance. The paper introduces models with varying levels of expressivity from abstract actions to constrained discrete predictions, demonstrating that more expressive models produce simpler trajectories that are faster to execute but may result in slightly reduced navigation success. Notably, the most expressive model increases the success rate by 4% over existing benchmarks, showcasing the potential of high-level waypoint guidance in complex environments.

Results indicate that lower expressivity action spaces improve success rates due to closer approximations of shortest paths, albeit producing trajectories with numerous stops and turns, challenging real-time execution feasibility. Real-world implications are highlighted through profiling a LoCoBot robot, conveying that abstract waypoint models dramatically reduce execution time and strain on robotic systems.

Implications and Future Directions

This research underscores the importance of choosing an appropriate action space that balances expressivity and execution efficiency. Importantly, it establishes a new state-of-the-art in the VLN-CE task, supporting the potential for sim-to-real transfer by providing actionable insights for further integration of language understanding with robotic control. The dual emphasis on both navigation success and execution time bridges the gap between abstract instruction parsing and practical use in robotic systems, paving the way for robust deployment in real-world applications.

Future research can build on these findings by exploring enhanced models integrating object semantics or dynamic adaptation to evolving environments, further refining waypoint prediction fidelity. Investigating these factors can contribute to developing seamless AI-driven navigation systems, optimizing performance across various robotic platforms. The demonstrated improvements and strategic framework provide a foundation for advancing sim-to-real methodologies, facilitating the transition from simulated success to practical utility in autonomous navigation systems.