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arxiv: 2502.13498 · v2 · submitted 2025-02-19 · 💻 cs.RO · cs.CV

Collision-Aware Object-Goal Visual Navigation via Two-Stage Deep Reinforcement Learning

Hongwu Wang , Shiwei Lian , Feitian Zhang This is my paper

Pith reviewed 2026-05-23 02:53 UTC · model grok-4.3

classification 💻 cs.RO cs.CV
keywords object-goal visual navigationdeep reinforcement learningcollision avoidancetwo-stage trainingcollision-free success rateAI2-THORegocentric vision
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The pith

A two-stage deep reinforcement learning method with a separate collision predictor raises collision-free success rates in object-goal visual navigation.

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

The paper establishes that standard object-goal navigation often ignores collisions, so it defines new metrics that count only collision-free successes and proposes training in two stages. In the first stage the agent learns to predict its own collisions from visual input; in the second stage that predictor guides policy learning so the agent reaches the target while staying clear of obstacles. This separation matters because it lets the same base navigation models improve on the new metrics without changing their overall architecture. Experiments in the AI2-THOR simulator and on physical robots show the gains hold across several models and transfer to real settings.

Core claim

The central claim is that a collision prediction module trained by direct supervision of collision states during exploration can be reused in a second training stage to produce navigation policies that achieve higher collision-free success rate (CF-SR) and collision-free success weighted by path length (CF-SPL) than the same models trained without the module.

What carries the argument

A collision prediction module trained in the first stage by supervising the agent's collision states and then inserted into the second-stage navigation policy to penalize or avoid predicted collisions.

If this is right

  • Multiple existing navigation models obtain higher CF-SR and CF-SPL after the two-stage procedure.
  • The framework produces policies that generalize from simulation to real-robot object-goal tasks.
  • Navigation evaluation now explicitly accounts for collisions rather than treating them as neutral or successful.
  • The same collision predictor can be reused across different target objects and starting positions.

Where Pith is reading between the lines

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

  • The separation of collision prediction from navigation might allow the predictor to be swapped for other safety modules without retraining the entire policy.
  • The approach could be tested in environments containing moving obstacles to check whether the static collision predictor still suffices.
  • If the collision predictor is made probabilistic, the second stage might trade off risk against path length in a more explicit way.

Load-bearing premise

The collision prediction module learned from exploration transfers to the navigation stage and improves collision-free performance without hurting the underlying task success.

What would settle it

Training the same navigation models with and without the collision prediction module in AI2-THOR and finding no statistically significant rise in CF-SR or CF-SPL would falsify the central claim.

Figures

Figures reproduced from arXiv: 2502.13498 by Feitian Zhang, Hongwu Wang, Shiwei Lian.

Figure 1
Figure 1. Figure 1: The illustration of the two-stage training method with collision prediction. In the first stage, the collision prediction module is trained by supervising [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: The learning curves of CF-SR using different collision avoidance [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 2
Figure 2. Figure 2: Three sampled navigation paths of L-sTDE [15] model before & [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: The CF-SR learning curves of ablation studies in the training set. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
read the original abstract

Object-goal visual navigation aims to reach a specific target object using egocentric visual observations. Recent deep reinforcement learning (DRL) approaches have achieved promising success rates but often neglect collisions during evaluation, limiting real-world deployment. To address this issue, this letter introduces a collision-aware evaluation metric, namely collision-free success rate (CF-SR), to explicitly measure navigation performance under collision constraints. In addition, collision-free success weighted by path length (CF-SPL) is adopted to further evaluate navigation efficiency. Furthermore, a two-stage DRL training framework with collision prediction is proposed to improve collision-free navigation performance. In the first stage, a collision prediction module is trained by supervising the agent's collision states during exploration. In the second stage, leveraging the trained collision prediction, the agent learns to navigate toward target objects while avoiding collision. Extensive experiments across multiple navigation models in the AI2-THOR environment demonstrate consistent improvements in both CF-SR and CF-SPL. Real-world experiments further validate the effectiveness and generalization capability of the proposed framework.

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 manuscript introduces collision-free success rate (CF-SR) and collision-free success weighted by path length (CF-SPL) as evaluation metrics for object-goal visual navigation. It proposes a two-stage DRL framework: stage 1 trains a collision prediction module via supervised learning on collision states collected during exploration; stage 2 uses the trained predictor to guide an agent toward target objects while avoiding collisions. The abstract states that experiments across multiple navigation models in AI2-THOR demonstrate consistent improvements in the new metrics, with real-world experiments validating generalization and effectiveness.

Significance. If the transfer of the collision predictor is shown to be effective and the reported gains are causally attributable to it rather than other training changes, the work would usefully address a practical gap in visual navigation by making collision avoidance explicit in both training and evaluation. The new metrics provide a clearer signal for real-world applicability than standard SR/SPL. The two-stage separation is a pragmatic design choice that avoids requiring collision signals in the primary task reward.

major comments (2)
  1. [Abstract] Abstract: the central claim of 'consistent improvements in both CF-SR and CF-SPL' across models and 'real-world experiments further validate' is presented without any quantitative values, baseline comparisons, number of models, ablation results, or statistical details. This absence prevents verification that the two-stage collision-prediction approach is the driver of the gains.
  2. [Method (two-stage DRL training framework)] Two-stage framework description: the transfer assumption—that the collision predictor trained on exploration trajectories generalizes to the learned navigation policy without introducing false positives/negatives or degrading task performance—is load-bearing for the contribution but receives no isolated validation, out-of-distribution tests, or integration details (reward shaping, input concatenation, or constraint). If exploration and navigation state distributions differ in velocity or interaction patterns, the predictor may not function as intended.
minor comments (1)
  1. [Abstract] Abstract: clarify whether CF-SR is simply the success rate computed only over collision-free episodes or a distinct formulation; the current phrasing 'collision-aware evaluation metric' is ambiguous.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major comment below and indicate revisions where appropriate to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim of 'consistent improvements in both CF-SR and CF-SPL' across models and 'real-world experiments further validate' is presented without any quantitative values, baseline comparisons, number of models, ablation results, or statistical details. This absence prevents verification that the two-stage collision-prediction approach is the driver of the gains.

    Authors: We agree that the abstract would be strengthened by including key quantitative details. The experiments section provides these (results across multiple models with reported gains in CF-SR and CF-SPL, plus real-world validation), but we will revise the abstract to incorporate representative values and the number of models tested while respecting length limits. revision: yes

  2. Referee: [Method (two-stage DRL training framework)] Two-stage framework description: the transfer assumption—that the collision predictor trained on exploration trajectories generalizes to the learned navigation policy without introducing false positives/negatives or degrading task performance—is load-bearing for the contribution but receives no isolated validation, out-of-distribution tests, or integration details (reward shaping, input concatenation, or constraint). If exploration and navigation state distributions differ in velocity or interaction patterns, the predictor may not function as intended.

    Authors: Section 3 describes the integration: the predictor output is concatenated with visual features for the policy and used in reward shaping via collision penalties. Overall system results support effectiveness, but we acknowledge the lack of isolated predictor validation or explicit OOD tests on navigation states. We will add an ablation isolating predictor accuracy and transfer performance in the revision. revision: yes

Circularity Check

0 steps flagged

Two-stage DRL training and metrics are independent; no reduction to inputs by construction

full rationale

The paper defines a two-stage process in which a collision prediction module is first trained via supervision on exploration collision states, then incorporated into a second-stage RL policy for goal-directed navigation. The new metrics CF-SR and CF-SPL are defined directly from success and path length under an explicit collision constraint and are not algebraically or statistically forced by the training procedure. No equations appear that equate a reported gain to a fitted parameter or prior output by construction. No self-citation is used to import a uniqueness theorem or ansatz that would render the central claim tautological. The derivation therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework assumes collision states are reliably observable and labelable during exploration and that the predictor generalizes to the navigation policy without additional regularization.

axioms (1)
  • domain assumption Collision states during exploration can be directly supervised to train a reliable prediction module.
    Invoked in the description of the first training stage.

pith-pipeline@v0.9.0 · 5711 in / 1110 out tokens · 22144 ms · 2026-05-23T02:53:22.919961+00:00 · methodology

discussion (0)

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