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arxiv: 2605.25006 · v1 · pith:5W2DXDJAnew · submitted 2026-05-24 · 💻 cs.RO · cs.LG· cs.NE

Convex-Neural RRT*: Fast and Reliable Learning-Guided Sampling for High-Quality Robot Path Planning

Hichem Cheriet , Badra Khellat Kihel , Samira Chouraqui , Bara J. Emran This is my paper

Pith reviewed 2026-06-30 00:36 UTC · model grok-4.3

classification 💻 cs.RO cs.LGcs.NE
keywords robot path planningRRT*neural guidancesampling-based planningconvex regionsmotion planningwaypoint prediction
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The pith

Extracting convex regions from neural waypoint predictions lets RRT* concentrate samples on promising areas and cut planning time while preserving path quality.

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

The paper develops Convex-Neural RRT* by first training a neural network to predict regions containing high-quality paths and then extracting convex candidate regions from those predictions. These convex sets direct the random sampling step of RRT* toward geometrically relevant locations without eliminating the algorithm's global exploration property. Experiments across eighteen maps show the resulting planner finishes faster than both classical RRT* and earlier neural RRT* variants, returns modestly shorter paths, and still succeeds in over 99 percent of trials. A reader would care because the approach directly addresses the practical bottleneck of long run times in sampling-based planners used for real robot navigation.

Core claim

Convex-Neural RRT* incorporates neural guidance to predict informative waypoint regions near high-quality paths. Convex candidate regions are extracted from these predictions, enabling the planner to concentrate exploration on geometrically relevant areas while preserving global exploration.

What carries the argument

Convex candidate regions extracted from neural predictions of waypoint regions near high-quality paths

If this is right

  • Computation time drops 30-75 percent versus other neural-guided RRT* methods and 88-98 percent versus LTA*.
  • Average path length improves by roughly 5 percent over classical RRT*, with larger gains in complex maps.
  • Success rate stays above 99 percent across different obstacle densities.
  • The planner remains probabilistically complete because convex guidance supplements rather than replaces random sampling.

Where Pith is reading between the lines

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

  • The same convex-extraction step could be inserted into other sampling-based planners such as PRM or FMT* to test whether similar speed-ups appear.
  • If the neural model is updated online from recent plans, the method might adapt to slowly changing environments without retraining from scratch.
  • In very high-dimensional configuration spaces the convex regions may reduce the volume that must be searched, offering a possible route around the curse of dimensionality.

Load-bearing premise

Neural predictions of waypoint regions are accurate enough that the extracted convex regions never exclude paths shorter or more feasible than those reachable by random sampling.

What would settle it

Run Convex-Neural RRT* on a map distribution where the neural model was trained on dissimilar obstacle layouts and check whether final path lengths exceed those of classical RRT* or success rate falls below 99 percent.

read the original abstract

Sampling-based algorithms for robot path planning offer probabilistic completeness and strong empirical convergence properties across environments with diverse obstacle configurations. However, in practice, these methods often require many iterations to obtain high-quality solutions. This paper proposes Convex-Neural RRT*, an enhanced RRT* variant that incorporates neural guidance to predict informative waypoint regions near high-quality paths. Convex candidate regions are extracted from these predictions, enabling the planner to concentrate exploration on geometrically relevant areas while preserving global exploration. The proposed algorithm is evaluated against Neural RRT*, Neural Informed RRT*, classical RRT*, and LTA* across three environment types and 18 benchmark maps. Experimental results show that Convex-Neural RRT* reduces computation time by 30-75% compared to neural-guided variants and up to 88-98% relative to LTA*, while achieving an average path length reduction of approximately 5% compared to classical RRT*, with larger improvements observed in complex environments. The method also maintains an overall success rate above 99% across varying obstacle densities. These findings indicate that convex-guided neural sampling provides an effective balance between computational efficiency and solution quality, supporting its applicability to time-sensitive robotic navigation tasks.

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

2 major / 1 minor

Summary. The paper proposes Convex-Neural RRT*, an RRT* variant that uses a neural network to predict waypoint regions near high-quality paths, extracts convex candidate regions from these predictions, and biases sampling toward those regions while retaining global exploration. It reports empirical results on 18 benchmark maps across three environment types, claiming 30-75% computation time reduction versus neural-guided baselines, up to 88-98% versus LTA*, an average 5% path-length improvement versus classical RRT* (larger in complex maps), and >99% success rate.

Significance. If the central empirical claims hold, the work would demonstrate a practical way to combine learned waypoint prediction with convex geometry to accelerate sampling-based planning without sacrificing solution quality. The evaluation across 18 maps in varied obstacle densities provides a reasonable breadth of testing. No machine-checked proofs, open reproducible code, or parameter-free derivations are described.

major comments (2)
  1. [§3] §3 (method): the description of predicting waypoint regions and extracting their convex hulls to bias sampling contains no formal argument or proof that the resulting convex sets are guaranteed to contain a path at least as short as the one discoverable by uniform sampling; if the neural prediction misses an optimal corridor, the reported ~5% path-length reduction relative to RRT* could be an artifact of the bias rather than an improvement.
  2. [Experimental results] Experimental results (across the 18-map suite): the performance numbers (30-75% time reduction, 5% path-length reduction, >99% success) are stated without reference to the neural training procedure, number of independent runs per map, standard deviations, or the precise operational definitions used for success rate and path length, so the statistical reliability of the central performance claims cannot be assessed from the reported data.
minor comments (1)
  1. [Abstract / §3] The abstract and method section would benefit from an explicit statement of the neural network architecture, loss function, and training data generation process.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We respond to each major comment below and note the changes planned for the revised manuscript.

read point-by-point responses

  1. Referee: [§3] §3 (method): the description of predicting waypoint regions and extracting their convex hulls to bias sampling contains no formal argument or proof that the resulting convex sets are guaranteed to contain a path at least as short as the one discoverable by uniform sampling; if the neural prediction misses an optimal corridor, the reported ~5% path-length reduction relative to RRT* could be an artifact of the bias rather than an improvement.

    Authors: We agree that Section 3 provides no formal proof or guarantee that the extracted convex sets will contain a path at least as short as one found by uniform sampling. Convex-Neural RRT* is a heuristic that biases sampling based on learned predictions while retaining a uniform sampling component for global exploration; it makes no claim of improved optimality guarantees beyond standard RRT*. The reported path-length reductions are empirical observations. We will add a limitations paragraph to Section 3 explicitly discussing the heuristic nature of the guidance and the possibility that poor neural predictions could affect solution quality. revision: partial

  2. Referee: [Experimental results] Experimental results (across the 18-map suite): the performance numbers (30-75% time reduction, 5% path-length reduction, >99% success) are stated without reference to the neural training procedure, number of independent runs per map, standard deviations, or the precise operational definitions used for success rate and path length, so the statistical reliability of the central performance claims cannot be assessed from the reported data.

    Authors: We acknowledge that the current manuscript does not sufficiently detail the neural training procedure, the number of independent runs, standard deviations, or precise metric definitions in the main experimental section. The revised version will expand Section 4 to include these elements: a clear description of the training setup, the number of runs performed, variance measures, and explicit definitions of success rate (finding a feasible path within the allotted time) and path length (optimized Euclidean length). revision: yes

Circularity Check

0 steps flagged

No circularity: empirical claims rest on benchmark comparisons, not self-referential derivations

full rationale

The paper describes an algorithmic method (neural waypoint prediction followed by convex region extraction to bias RRT* sampling) and reports performance via direct empirical runs against Neural RRT*, Neural Informed RRT*, classical RRT*, and LTA* on 18 maps. No equations, fitted parameters, or self-citations are shown that would make reported speedups, path lengths, or success rates equivalent to inputs by construction. The central claims are falsifiable external benchmarks, not reductions of the form 'prediction = fit'. This is the normal non-circular case for an applied robotics paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

The abstract introduces no explicit free parameters, mathematical axioms, or new physical entities; the method rests on the unstated assumption that a trained neural predictor can reliably identify useful convex regions without external validation of that predictor's accuracy.

invented entities (1)
  • Convex candidate regions extracted from neural predictions no independent evidence
    purpose: To focus sampling on geometrically relevant areas near high-quality paths while preserving global exploration
    This is a new procedural construct introduced by the method; the abstract provides no independent evidence outside the reported experiments that these regions are reliably informative.

pith-pipeline@v0.9.1-grok · 5765 in / 1322 out tokens · 45030 ms · 2026-06-30T00:36:27.263064+00:00 · methodology

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Reference graph

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