# P3Nav: End-to-End Perception, Prediction and Planning for Vision-and-Language Navigation

arXiv: https://arxiv.org/abs/2603.17459 · HTML: https://mlsanity.com/paper/2603.17459/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2026.

## Built on by (most cited first, 2 of 2)

- [What Limits Vision-and-Language Navigation ?](https://mlsanity.com/paper/2605.13328/) (2026)
- [HCSG: Human-Centric Semantic-Geometric Reasoning for Vision-Language Navigation](https://mlsanity.com/paper/2605.13321/) (2026)

## Abstract (arXiv)

In Vision-and-Language Navigation (VLN), an agent is required to plan a path to the target specified by the language instruction, using its visual observations. Consequently, prevailing VLN methods primarily focus on building powerful planners through visual-textual alignment. However, these approaches often bypass the imperative of comprehensive scene understanding prior to planning, leaving the agent with insufficient perception or prediction capabilities. Thus, we propose P3Nav, a novel end-to-end framework integrating perception, prediction, and planning in a unified pipeline to strengthen the VLN agent's scene understanding and boost navigation success. Specifically, P3Nav augments perception by extracting complementary cues from object-level and map-level perspectives. Subsequently, our P3Nav predicts waypoints to model the agent's potential future states, endowing the agent with intrinsic awareness of candidate positions during navigation. Conditioned on these future waypoints, P3Nav further forecasts semantic map cues, enabling proactive planning and reducing the strict reliance on purely historical context. Integrating these perceptual and predictive cues, a holistic planning module finally carries out the VLN tasks. Extensive experiments demonstrate that our P3Nav achieves new state-of-the-art performance on the REVERIE, R2R-CE, and RxR-CE benchmarks.