Source-linked AI summary
Self-Monitoring Navigation Agent via Auxiliary Progress Estimation
Chih-Yao Ma, Jiasen Lu, Zuxuan Wu, Ghassan AlRegib, Zsolt Kira, Richard Socher, Caiming Xiong
TL;DR
VLN requires agents to navigate unknown realistic environments while determining which instructions are completed, which comes next, and how far they have progressed. The paper introduces a self-monitoring agent that jointly grounds visual and textual instructions and monitors progress, achieving an 8% absolute success-rate improvement on the unseen test set and new state-of-the-art benchmark performance.
Problem
VLN agents must follow instructions in unknown environments, but previous approaches generally lack awareness of the next instruction and navigation progress.
Method
The agent combines visual-textual co-grounding of past, current, and next instructions with a progress monitor conditioned on grounded-instruction positions and weights.
Results
8% absolute improvement in success rate on the unseen test set yields new state-of-the-art performance and ranks the method #1 on the challenge leaderboard.
Takeaways & Limitations
Progress monitoring significantly improves success rate in ablations and is key to surpassing the state of the art without data augmentation.
Takeaways & Limitations
A qualitative failure shows the progress monitor can assign a much higher completeness estimate when the agent is not correctly following the instruction.
Abstract
from arXiv · showhide
The Vision-and-Language Navigation (VLN) task entails an agent following navigational instruction in photo-realistic unknown environments. This challenging task demands that the agent be aware of which instruction was completed, which instruction is needed next, which way to go, and its navigation progress towards the goal. In this paper, we introduce a self-monitoring agent with two complementary components: (1) visual-textual co-grounding module to locate the instruction completed in the past, the instruction required for the next action, and the next moving direction from surrounding images and (2) progress monitor to ensure the grounded instruction correctly reflects the navigation progress. We test our self-monitoring agent on a standard benchmark and analyze our proposed approach through a series of ablation studies that elucidate the contributions of the primary components. Using our proposed method, we set the new state of the art by a significant margin (8% absolute increase in success rate on the unseen test set). Code is available at https://github.com/chihyaoma/selfmonitoring-agent .
1 INTRODUCTION
VLN requires agents to follow natural-language instructions through unknown realistic environments while tracking completed, ongoing, and upcoming instructions. The proposed self-monitoring agent combines visual-textual co-grounding with progress monitoring to coordinate action selection and navigation progress.
- VLN requires an agent to follow natural-language instructions from a starting location to a target location in an unknown realistic environment.
- Instruction transitions are ambiguous because completing an ongoing action often depends on identifying the next action.The example illustrates why the agent must track both completed or ongoing instructions and the instruction needed next.
- Previous VLN approaches generally do not track which instruction is next or the agent's progress towards the goal.The paper also reports that a baseline attentional mechanism does not successfully track this information through time.
- The proposed agent jointly grounds visual direction, completed or ongoing text, and the next action while monitoring progress toward the goal.Its two complementary modules are visual-textual co-grounding and a progress monitor.
- 8% absolute improvement in success rate on the unseen test set establishes new state-of-the-art performance and ranks the method first on the challenge leaderboard.The paper reports state-of-the-art results on both seen and unseen environments of the standard benchmark.
2 SELF-MONITORING NAVIGATION AGENT
The self-monitoring agent jointly grounds instruction text and surrounding visual observations, then monitors progress to align grounded instructions with movement toward the goal.
- The agent combines textual grounding, visual grounding, progress monitoring, and action selection in a unified navigation architecture.Textual grounding tracks completed, ongoing, and potentially next instruction parts; visual grounding summarizes surrounding images; progress monitoring aligns grounding with goal progress.
- Visual and textual co-grounding: At each step, an LSTM decoder integrates the attended panoramic image, previous action, and current grounded instruction to produce the context for action selection.The previous encoder context supports grounding, while the current context supports the next action.
- Visual and textual co-grounding: Textual and visual attention identify relevant instruction words and surrounding views, while action selection scores navigable directions using grounded instruction and hidden-state correlations.Textual attention incorporates positional encoding; visual attention uses the previous hidden state; direction probabilities are computed from correlations with navigable image features.
- Visual and textual co-grounding: Visual-textual co-grounding uses instruction history, observed images, and selected actions to ground both modalities simultaneously.A shared hidden state contains grounded information from textual and visual modalities, unlike attention based only on image history.
- Progress monitor: The progress monitor estimates instruction-following completeness from grounded-image history, current observations, and textual-attention positions.Its inputs include recurrent states, grounded surrounding images, and textual-grounding attention weights, exploiting alignment between instruction positions and navigation progress.
- Training and inference: Training combines action-selection cross-entropy with progress-monitor mean squared error, while progress inference revisits choices when the monitor predicts decreasing progress.The progress target is normalized distance to the goal, beginning at 0 and approaching 1 as the agent gets closer.
3 EXPERIMENTS
Experiments evaluate the self-monitoring agent on R2R, compare it with prior methods, and examine its grounding, progress monitoring, inference, augmentation, and qualitative behavior. The method improves success and produces instruction grounding that advances through the instruction while progress estimates rise toward the goal.
- Dataset and metrics: The R2R dataset contains 7,189 paths with three human-written instructions per path, divided into training, seen-validation, unseen-validation, and unseen-test sets.
- Dataset and metrics: Success Rate measures final positions within 3m of the goal, while Navigation Error, Oracle Success Rate, and SPL provide complementary evaluation measures.
- Comparison with prior art: 70% SR on seen environments and 57% on unseen environments exceed the existing best results of 63% and 50%, respectively, without data augmentation.
- Grounding analysis: Grounded instruction positions form a near-diagonal progression through the instruction, unlike the baseline, indicating sequential use of instruction attention during navigation.
- Ablation study: The co-grounding agent outperforms the baseline because its LSTM and action prediction use both textually and visually grounded information.
- Ablation study: The progress monitor further improves performance, especially on unseen environments, by encouraging grounded instructions to reflect navigation progress.
- Ablation study: Progress inference lets the agent use estimated progress to decide when to return to the last viewpoint, improving SR by around 2% on both seen and unseen environments.
- Ablation study: Beam search without progress monitoring improves unseen SR by 7%, whereas beam search with progress estimation improves it by 13%.
4 RELATED WORK
VLN research combines vision and language for embodied navigation, while prior approaches address instruction following through sequence models, grounding, planning, and data augmentation.
- Vision-and-language navigation asks agents to execute sophisticated natural-language instructions in photo-realistic environments.
- Prior VLN methods include sequence-to-sequence translation, guided feature transformation for textual grounding, planned-head reinforcement learning, and speaker-based instruction augmentation.
- Visual grounding localizes image regions relevant to linguistic descriptions and supports tasks including captioning, visual question answering, relationship detection, and referring expressions.
5 CONCLUSION
The paper presents self-monitoring as a VLN agent combining visual-textual co-grounding with progress monitoring, achieving state-of-the-art performance on seen and unseen environments.
- The self-monitoring agent combines visual-textual co-grounding with a progress monitor.
- The co-grounding module identifies completed and next instructions and the moving direction from surrounding images.
- The progress monitor estimates instruction-following completeness using grounded-instruction positions and weights.
- The approach achieves state-of-the-art performance on the standard Room-to-Room dataset in seen and unseen environments.
SUPPLEMENTARY MATERIALS
Supplementary results compare performance without beam search and illustrate how grounding and progress monitoring support successful navigation while exposing failure cases.
- Comparison with prior art without beam search: 13% improvement over the best existing approach was achieved on the unseen test set when progress inference was used for action selection without beam search.Greedy decoding improved success rate by 9% on validation unseen and 8% on the test set; progress inference added 5% on the test set.
- Submission to Vision and Language Navigation Challenge: 61% success rate ranked the full proposed approach #1 on the unseen test server at the time of writing.
- Qualitative results: The agent progressively shifts attention from completed actions to upcoming instructions, then grounds on “stop” to end successful trajectories.
- Qualitative results: In an ambiguous two-room case, the agent rejected an initial room and stopped in another room matching the instruction’s rug description.
- Qualitative results: Repeated actions around stairs require step-by-step instruction following and completeness assessment to avoid stopping before the goal.
- Failure cases: A failure case shows the progress monitor reporting only 16% completion after the agent missed an instructed left turn.
- Failure cases: Another failure occurred when attention linked “go down” to no previously mentioned stairs, while the monitor estimated higher completeness and missed the incorrect following.