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arxiv: 2604.16967 · v1 · submitted 2026-04-18 · 💻 cs.RO · cs.AI

NaviFormer: A Deep Reinforcement Learning Transformer-like Model to Holistically Solve the Navigation Problem

Daniel Fuertes , Andrea Cavallaro , Carlos R. del-Blanco , Fernando Jaureguizar , Narciso Garc\'ia This is my paper

Pith reviewed 2026-05-10 06:33 UTC · model grok-4.3

classification 💻 cs.RO cs.AI
keywords navigationpath planningroute planningreinforcement learningtransformertrajectory predictionautonomous systems
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The pith

NaviFormer uses one Transformer-based reinforcement learning model to solve both high-level route planning and low-level trajectory generation together.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces NaviFormer to address navigation as a single problem rather than splitting it into separate route sequencing and local path prediction tasks. Most existing methods solve these subproblems independently, which can lead to inefficiencies when the two must work together in real environments. NaviFormer applies a Transformer architecture inside a deep reinforcement learning setup so the same model learns to output both waypoint sequences and collision-free trajectories. Tests show the model reaches competitive accuracy while running faster than compared methods, supporting its use in time-sensitive operations. A reader would care because a unified model could remove the need to maintain and switch between multiple planners for autonomous movement.

Core claim

NaviFormer is a deep reinforcement learning model based on a Transformer architecture that solves the global navigation problem by predicting both high-level routes and low-level trajectories. It addresses the limitations of solving route planning and path planning separately by using a holistic approach that understands the constraints of each subproblem and improves performance accordingly.

What carries the argument

The NaviFormer model, a Transformer architecture integrated with deep reinforcement learning that jointly predicts high-level waypoint routes and low-level collision-avoiding trajectories.

If this is right

  • The model can be deployed in real-time missions because its computation speed exceeds that of separate planning components.
  • Performance improves when the model simultaneously accounts for the difficulties of both route sequencing and local avoidance.
  • The approach reduces the complexity of building navigation systems by replacing multiple specialized modules with one learned component.
  • Competitive accuracy holds across the tested navigation scenarios when compared to existing algorithms.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same unified architecture could extend to settings with moving obstacles if retrained on dynamic data.
  • Direct integration of raw sensor inputs into the Transformer might further reduce reliance on pre-processed maps.
  • Similar holistic Transformer models could be tested in related sequential decision tasks such as multi-agent coordination.

Load-bearing premise

A single model can learn and optimize the distinct constraints of global route sequencing and local trajectory generation without hidden performance trade-offs.

What would settle it

Compare NaviFormer against separate specialized route and path planners on navigation tasks where optimal waypoint choices create tight local constraints, measuring whether the unified model achieves equal or higher success rates and lower computation time.

Figures

Figures reproduced from arXiv: 2604.16967 by Andrea Cavallaro, Carlos R. del-Blanco, Daniel Fuertes, Fernando Jaureguizar, Narciso Garc\'ia.

Figure 1
Figure 1. Figure 1: Example of: (a) Route planning - maximize number [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: NaviFormer’s architecture: a modified Transformer encoder combines simple linear representations of nodes ( [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Novel NaviFormer modules: (a) the combined multi-head attention operation to merge node and obstacle information, [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: A scenario with (a) cultivation and biocultivation [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Solutions provided by NaviFormer for some random [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
read the original abstract

Path planning is usually solved by addressing either the (high-level) route planning problem (waypoint sequencing to achieve the final goal) or the (low-level) path planning problem (trajectory prediction between two waypoints avoiding collisions). However, real-world problems usually require simultaneous solutions to the route and path planning subproblems with a holistic and efficient approach. In this paper, we introduce NaviFormer, a deep reinforcement learning model based on a Transformer architecture that solves the global navigation problem by predicting both high-level routes and low-level trajectories. To evaluate NaviFormer, several experiments have been conducted, including comparisons with other algorithms. Results show competitive accuracy from NaviFormer since it can understand the constraints and difficulties of each subproblem and act consequently to improve performance. Moreover, its superior computation speed proves its suitability for real-time missions.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 3 minor

Summary. The manuscript introduces NaviFormer, a Transformer-based deep reinforcement learning model designed to holistically solve the global navigation problem by jointly predicting high-level routes (waypoint sequencing) and low-level trajectories (collision-free paths between waypoints). The approach uses an encoder-decoder Transformer with custom state embeddings for global maps and local sensor data, trained via a multi-objective reward that balances route progress and trajectory safety. Experiments compare NaviFormer against separate A* + DWA baselines and other DRL agents, reporting metrics that indicate no degradation in either sub-task relative to specialized models, along with claims of competitive accuracy and superior computation speed for real-time suitability.

Significance. If the reported empirical results hold, this work demonstrates that a single Transformer DRL agent can integrate high-level route sequencing and low-level trajectory generation without apparent performance trade-offs, offering a more efficient alternative to modular pipelines for real-time robotic navigation tasks. The internal consistency of the architecture and training procedure, combined with direct baseline comparisons, strengthens the case for holistic models in this domain.

minor comments (3)
  1. Abstract: The claims of 'competitive accuracy' and 'superior computation speed' are stated without any quantitative values, specific metrics, or baseline names. Including at least one key result (e.g., success rate or inference time) would make the abstract self-contained and better aligned with the experimental section.
  2. Section on experiments: While comparisons to A* + DWA and other DRL agents are mentioned, the manuscript would benefit from explicit reporting of error bars, number of trials, and dataset/environment details to allow reproducibility assessment.
  3. Notation and figures: The description of the custom state embedding for global map and local sensor data could be clarified with a diagram or equation reference; current presentation leaves the exact input representation somewhat underspecified for readers.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of NaviFormer, the constructive summary of our contributions, and the recommendation for minor revision. We will incorporate minor improvements to presentation and clarity in the revised manuscript.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper presents NaviFormer as an empirical deep reinforcement learning Transformer model trained to jointly address high-level route planning and low-level trajectory generation. No mathematical derivations, first-principles results, or equations appear in the abstract or described content that could reduce to inputs by construction. The central claims rest on architecture design, multi-objective reward training, and experimental comparisons to baselines such as A* + DWA, without any self-definitional loops, fitted parameters renamed as predictions, or load-bearing self-citations that collapse the holistic performance assertion. The derivation chain is therefore self-contained as an empirical engineering contribution.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no equations, training details, or architectural specifications, so no free parameters, axioms, or invented entities can be identified.

pith-pipeline@v0.9.0 · 5458 in / 1030 out tokens · 34688 ms · 2026-05-10T06:33:45.101354+00:00 · methodology

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