pith. sign in

arxiv: 2605.02528 · v1 · submitted 2026-05-04 · 💻 cs.RO · cs.LG

Beyond Specialization: Robust Reinforcement Learning Navigation via Procedural Map Generators

Christian Jestel , Nicolas Bach , Marvin Wiedemann , Jan Finke , Peter Detzner This is my paper

Pith reviewed 2026-05-08 17:39 UTC · model grok-4.3

classification 💻 cs.RO cs.LG
keywords reinforcement learningrobot navigationprocedural generationrobustnesssim-to-realpath planningLiDAR
0
0 comments X

The pith

RL navigation policies trained on mixed procedural generators reach 91.5% average success across layouts

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

The paper establishes that a single deep reinforcement learning policy for LiDAR navigation, when trained on the combined outputs of four procedural map generators, achieves 91.5% mean success on held-out maps from any of the generators. This matters because single-generator specialist policies overfit badly, dropping as low as 3.3% when tested on a different layout type. The work shows that A* path-planner subgoal inputs supply most of the robustness gain, lifting performance above both a plain feedforward network and one using recurrence. The learned policies also keep high success at 2 m/s speeds where a classical controller collapses, because they adapt velocity. Real-robot tests on a RoboMaster platform confirm that the policies transfer to cluttered physical arenas.

Core claim

A policy trained on the combined set of sparse, maze, graph, and Wave Function Collapse generators achieves 91.5 +/- 1.1% mean success rate across 1000 seeded maps per generator. Specialist policies fail dramatically on unseen generator types, with sparse-trained dropping to 3.3% on mazes. A* path-planner subgoal inputs raise success from 90.2% feedforward to 98.9 +/- 0.4%, outperforming GRU recurrence. The DRL policies maintain high performance at 2.0 m/s where a classical controller drops to 24.9%, due to learned speed adaptation. Real-world tests on RoboMaster show transfer in cluttered arenas but note maze-like failures mitigated by recurrence.

What carries the argument

The four guaranteed-navigable procedural generators (sparse, maze, graph, Wave Function Collapse) integrated into the MuRoSim 2D LiDAR simulator, together with A* path-planner subgoals supplied as policy inputs.

Load-bearing premise

The four procedural generators create enough variety that policies trained in the 2D simulator will generalize to real-world navigation beyond the tested arena.

What would settle it

A new test in which the combined-generator policy achieves low success on a fifth, previously unseen procedural generator type or in a real-world layout with different structure than the training arenas would show the generalization does not hold.

Figures

Figures reproduced from arXiv: 2605.02528 by Christian Jestel, Jan Finke, Marvin Wiedemann, Nicolas Bach, Peter Detzner.

Figure 1
Figure 1. Figure 1: A Deep Reinforcement Learning (DRL) policy navigat view at source ↗
Figure 2
Figure 2. Figure 2: Example environments from the four procedural map generators. (a) Sparse: randomly placed obstacles of varying shape view at source ↗
Figure 3
Figure 3. Figure 3: Trajectories of the five cross-evaluation policies on one representative map per environment type (left to right: sparse, view at source ↗
Figure 4
Figure 4. Figure 4: Trajectories of the four DRL configurations and the view at source ↗
Figure 5
Figure 5. Figure 5: Sim-to-real runs on the RoboMaster.1 (a) Third-person view of the cluttered open arena used for the combined policy. (b) RViz snapshot of the maze-like environment used to compare the sparse, combined, and GRU policies, showing the slam_toolbox map (gray), the live RPLidar scan (red), and the operator-set goal pose (green). feedforward subgoal variant raises mean success from 90.2 % to 98.9 % and nearly el… view at source ↗
read the original abstract

Deep reinforcement learning (DRL) navigation policies often overfit to the structure of their training environments, as environmental diversity is typically constrained by the manual effort required to design diverse scenarios. While procedural map generation offers scalable diversity, no prior work systematically compares how different generator types affect policy generalization. We integrate four generators (sparse, maze, graph, and Wave Function Collapse) with guaranteed navigability into MuRoSim, a 2D simulator focusing on training efficiency for LiDAR-based navigation. We cross-evaluate five navigation policies on 1000 seeded maps per generator across three training seeds. Results show a strongly asymmetric cross-generator transfer: a specialist trained on sparse layouts falls to 3.3% success on mazes, whereas a policy trained on the combined generator set achieves 91.5 +/- 1.1% mean success. We further demonstrate that A* path-planner subgoal inputs are the dominant factor for robustness, raising success from the 90.2 +/- 1.4% feedforward baseline to 98.9 +/- 0.4% and outperforming GRU recurrence, which only improves the reactive baseline. The DRL policies outperform a classical Carrot+A* controller, which matches their success only at low speeds (1.0 m/s) but collapses to 24.9% at 2.0 m/s. This highlights learned speed adaptation as the decisive advantage of the learned approach. Real-world experiments on a RoboMaster confirm sim-to-real transfer in a cluttered arena, while a maze-like layout exposes remaining failure modes that recurrence helps mitigate.

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

3 major / 2 minor

Summary. The paper claims that integrating four procedural map generators (sparse, maze, graph, and Wave Function Collapse) into the MuRoSim 2D LiDAR simulator enables training of DRL navigation policies with strong cross-generator generalization, reaching 91.5 +/- 1.1% mean success on held-out maps; adding A* path-planner subgoals further raises performance to 98.9 +/- 0.4%, outperforming both feedforward baselines and GRU recurrence, while learned policies exceed a classical Carrot+A* controller at higher speeds. Preliminary real-world RoboMaster trials are said to confirm sim-to-real transfer in a cluttered arena, with recurrence mitigating failures in a maze-like layout.

Significance. If the quantitative results hold, the work provides a concrete demonstration that diverse procedural generation can mitigate overfitting in DRL navigation and that hybrid A*+policy inputs are more effective for robustness than recurrence alone. The systematic cross-generator evaluation on 1000 seeded maps per generator across three seeds, together with the speed-dependent comparison to the classical controller, supplies a useful benchmark and falsifiable empirical pattern for the field. The emphasis on learned speed adaptation as the key advantage over classical methods is a clear, actionable insight.

major comments (3)
  1. [Abstract / real-world experiments] Abstract and real-world experiments paragraph: the claim of sim-to-real transfer is supported only by qualitative statements (confirmation in cluttered arena, exposure of failure modes in maze-like layout) with no reported success rates, standard deviations, speed-sweep data, or direct comparisons against the classical controller under identical real-world conditions; this is load-bearing for the generalization claim beyond the simulator.
  2. [Methods] Methods section (implied by reported results): success rates with standard deviations are presented (e.g., 91.5 +/- 1.1%, 98.9 +/- 0.4%) yet no policy architectures, training hyperparameters, exact data exclusion rules, or statistical tests are described, preventing independent assessment of whether the cross-generator asymmetry and A* improvement are reproducible or sensitive to implementation choices.
  3. [Results / cross-generator evaluation] Results on cross-generator transfer: all headline numbers (91.5% combined, 98.9% with A*) are obtained inside MuRoSim on maps drawn from the same four generator families used for training; the manuscript does not test whether the learned policy or A*+policy combination survives sensor noise, 3-D geometry, or unmodeled dynamics outside these 2-D, noise-free generators, which directly limits the robustness interpretation.
minor comments (2)
  1. [Abstract] The abstract and results would benefit from an explicit definition of 'success' (e.g., goal reaching within time limit, collision-free) and the precise LiDAR observation model used in MuRoSim.
  2. [Results figures/tables] Table or figure captions for the cross-generator matrix should include the exact number of evaluation episodes per cell and whether the three training seeds are averaged or shown separately.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their insightful comments on our manuscript. We provide point-by-point responses to the major comments and indicate the revisions we will undertake to address the concerns raised.

read point-by-point responses

  1. Referee: [Abstract / real-world experiments] Abstract and real-world experiments paragraph: the claim of sim-to-real transfer is supported only by qualitative statements (confirmation in cluttered arena, exposure of failure modes in maze-like layout) with no reported success rates, standard deviations, speed-sweep data, or direct comparisons against the classical controller under identical real-world conditions; this is load-bearing for the generalization claim beyond the simulator.

    Authors: We agree that the real-world results are presented qualitatively and lack quantitative metrics such as success rates or comparisons. These experiments were preliminary and intended to provide initial evidence of transfer rather than a full validation. In the revised manuscript, we will update the abstract and the real-world experiments section to explicitly note the qualitative nature of these trials and to temper the generalization claims accordingly. We will also add a discussion of the limitations of the current real-world evaluation and suggest directions for more rigorous future validation. revision: partial

  2. Referee: [Methods] Methods section (implied by reported results): success rates with standard deviations are presented (e.g., 91.5 +/- 1.1%, 98.9 +/- 0.4%) yet no policy architectures, training hyperparameters, exact data exclusion rules, or statistical tests are described, preventing independent assessment of whether the cross-generator asymmetry and A* improvement are reproducible or sensitive to implementation choices.

    Authors: The referee is correct that comprehensive methodological details are necessary for reproducibility. We will revise the Methods section to include complete specifications of the policy network architectures, all training hyperparameters, the exact procedures for map generation and data exclusion, and the statistical methods used to compute the reported means and standard deviations. This will enable readers to fully assess and potentially reproduce the cross-generator transfer results. revision: yes

  3. Referee: [Results / cross-generator evaluation] Results on cross-generator transfer: all headline numbers (91.5% combined, 98.9% with A*) are obtained inside MuRoSim on maps drawn from the same four generator families used for training; the manuscript does not test whether the learned policy or A*+policy combination survives sensor noise, 3-D geometry, or unmodeled dynamics outside these 2-D, noise-free generators, which directly limits the robustness interpretation.

    Authors: We acknowledge that our evaluation is performed entirely within the 2D MuRoSim environment using the four procedural generators. The core contribution lies in showing that training on a diverse set of these generators improves generalization across them, which is a controlled way to study robustness to map structure variation. We will revise the discussion section to more clearly delimit the scope of our robustness claims to the 2D simulated setting and to highlight that extension to noisy, 3D, or real-world dynamics beyond the preliminary trials remains an open challenge. No new experiments outside the current simulator are feasible within the scope of this work, but we believe the benchmark provided is valuable as is. revision: partial

Circularity Check

0 steps flagged

No circularity: all quantitative claims are direct empirical measurements on held-out maps.

full rationale

The paper reports only experimental results: policies are trained on maps from four procedural generators and evaluated on 1000 seeded held-out maps per generator. Success rates such as 91.5 +/- 1.1% (combined training) and 98.9 +/- 0.4% (A* subgoals) are measured outcomes, not derived quantities. No equations, ansatzes, uniqueness theorems, or self-citations are used to obtain these figures; the cross-generator transfer and comparisons to baselines (feedforward, GRU, classical controller) are straightforward statistical evaluations. The sim-to-real section is qualitative and does not alter the empirical nature of the core results. No step reduces a claimed prediction to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central empirical claim rests on the domain assumption that the chosen procedural generators collectively span the structural variation needed for real-world generalization; no explicit free parameters or invented entities are introduced beyond standard RL training choices.

axioms (2)
  • domain assumption The four procedural generators produce maps whose structural diversity is representative of real-world navigation environments
    Invoked when claiming that combined-generator training yields robust policies that transfer beyond simulation.
  • domain assumption 2D LiDAR simulation in MuRoSim sufficiently captures the dynamics and sensing of the real RoboMaster platform for navigation policy transfer
    Required for the sim-to-real claims and the comparison against the classical controller.

pith-pipeline@v0.9.0 · 5594 in / 1540 out tokens · 58586 ms · 2026-05-08T17:39:26.595370+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

26 extracted references · 26 canonical work pages

  1. [1]

    The dynamic window approach to collision avoidance,

    D. Fox, W. Burgard, and S. Thrun, “The dynamic window approach to collision avoidance,”IEEE Robotics & Automation Magazine, vol. 4, no. 1, pp. 23–33, Mar. 1997

    work page 1997

  2. [2]

    Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation,

    L. Tai, G. Paolo, and M. Liu, “Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation,” in2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Sep. 2017, pp. 31–36

    work page 2017

  3. [3]

    Obtaining Robust Control and Navigation Policies for Multi-robot Navigation via Deep Reinforcement Learning,

    C. Jestel, H. Surmann, J. Stenzel, O. Urbann, and M. Brehler, “Obtaining Robust Control and Navigation Policies for Multi-robot Navigation via Deep Reinforcement Learning,” in2021 7th International Conference on Automation, Robotics and Applications (ICARA), Feb. 2021, pp. 48–54

    work page 2021

  4. [4]

    A Survey of Zero-shot Generalisation in Deep Reinforcement Learning,

    R. Kirk, A. Zhang, E. Grefenstette, and T. Rockt ¨aschel, “A Survey of Zero-shot Generalisation in Deep Reinforcement Learning,”Journal of Artificial Intelligence Research, vol. 76, pp. 201–264, Jan. 2023

    work page 2023

  5. [5]

    Leveraging Procedural Generation to Benchmark Reinforcement Learning,

    K. Cobbe, C. Hesse, J. Hilton, and J. Schulman, “Leveraging Procedural Generation to Benchmark Reinforcement Learning,” inProceedings of the 37th International Conference on Machine Learning. PMLR, Nov. 2020, pp. 2048–2056

    work page 2020

  6. [6]

    MuRoSim – A Fast and Efficient Multi-Robot Simulation for Learning- based Navigation,

    C. Jestel, K. R ¨osner, N. Dietz, N. Bach, J. Eßer, J. Finke, and O. Urbann, “MuRoSim – A Fast and Efficient Multi-Robot Simulation for Learning- based Navigation,” in2024 IEEE International Conference on Robotics and Automation (ICRA), May 2024, pp. 16 881–16 887

    work page 2024

  7. [7]

    DRL-VO: Learning to Navigate Through Crowded Dynamic Scenes Using Velocity Obstacles,

    Z. Xie and P. Dames, “DRL-VO: Learning to Navigate Through Crowded Dynamic Scenes Using Velocity Obstacles,”IEEE Transac- tions on Robotics, vol. 39, no. 4, pp. 2700–2719, Aug. 2023

    work page 2023

  8. [8]

    Distributed multi-robot collision avoidance via deep reinforcement learning for navigation in complex scenarios,

    T. Fan, P. Long, W. Liu, and J. Pan, “Distributed multi-robot collision avoidance via deep reinforcement learning for navigation in complex scenarios,”The International Journal of Robotics Research, vol. 39, no. 7, pp. 856–892, Jun. 2020

    work page 2020

  9. [9]

    A Review of Nine Physics Engines for Reinforcement Learning Research,

    M. Kaup, C. Wolff, H. Hwang, J. Mayer, and E. Bruni, “A Review of Nine Physics Engines for Reinforcement Learning Research,” Aug. 2024

    work page 2024

  10. [10]

    Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning,

    NVIDIA, M. Mittal, P. Roth, J. Tigue, A. Richard, O. Zhang, P. Du, A. Serrano-Mu ˜noz, X. Yao, R. Zurbr ¨ugg, N. Rudin, L. Wawrzyniak, M. Rakhsha, A. Denzler, E. Heiden, A. Borovicka, O. Ahmed, I. Aki- nola, A. Anwar, M. T. Carlson, J. Y . Feng, A. Garg, R. Gasoto, L. Gulich, Y . Guo, M. Gussert, A. Hansen, M. Kulkarni, C. Li, W. Liu, V . Makoviychuk, G. ...

    work page 2025

  11. [11]

    CAMAR: Continuous Actions Multi-Agent Routing,

    A. Pshenitsyn, A. Panov, and A. Skrynnik, “CAMAR: Continuous Actions Multi-Agent Routing,” Nov. 2025

    work page 2025

  12. [12]

    ProcTHOR: Large- Scale Embodied AI Using Procedural Generation,

    M. Deitke, E. VanderBilt, A. Herrasti, L. Weihs, K. Ehsani, J. Salvador, W. Han, E. Kolve, A. Kembhavi, and R. Mottaghi, “ProcTHOR: Large- Scale Embodied AI Using Procedural Generation,”Advances in Neural Information Processing Systems, vol. 35, pp. 5982–5994, Dec. 2022

    work page 2022

  13. [13]

    Demonstrating Arena 5.0: A Photorealistic ROS2 Simula- tion Framework for Developing and Benchmarking Social Navigation,

    L. K ¨astner, V . Shcherbyna, H. Soh, G. Truong, D. Anh, T. Kien, T. Seeger, A. Martban, V . Lam, N. Hung, P. Tung, T. An, E. Wiese, and M. Schreff, “Demonstrating Arena 5.0: A Photorealistic ROS2 Simula- tion Framework for Developing and Benchmarking Social Navigation,” inRobotics: Science and Systems XXI. Robotics: Science and Systems Foundation, Jun. 2025

    work page 2025

  14. [14]

    2D Grid Map Generation for Deep-Learning-based Navigation Approaches,

    G. O. Flores-Aquino, J. Duvier D ´ıaz Ortega, R. Y . Almazan Arvizu, O. Octavio Gutierrez-Frias, R. L. Mu ˜noz, and J. Irving Vasquez- Gomez, “2D Grid Map Generation for Deep-Learning-based Navigation Approaches,” in2021 International Conference on Mechatronics, Elec- tronics and Automotive Engineering (ICMEAE), Nov. 2021, pp. 66–70

    work page 2021

  15. [15]

    WaveFunctionCollapse is constraint solving in the wild,

    I. Karth and A. M. Smith, “WaveFunctionCollapse is constraint solving in the wild,” inProceedings of the 12th International Conference on the Foundations of Digital Games, ser. FDG ’17. New York, NY , USA: Association for Computing Machinery, Aug. 2017, pp. 1–10

    work page 2017

  16. [16]

    Shortest Connection Networks And Some Generalizations,

    R. C. Prim, “Shortest Connection Networks And Some Generalizations,” Bell System Technical Journal, vol. 36, no. 6, pp. 1389–1401, Nov. 1957

    work page 1957

  17. [17]

    Level Graph – Incremental Procedural Generation of Indoor Levels using Minimum Spanning Trees,

    B. V on Rymon Lipinski, S. Seibt, J. Roth, and D. Abe, “Level Graph – Incremental Procedural Generation of Indoor Levels using Minimum Spanning Trees,” in2019 IEEE Conference on Games (CoG). London, United Kingdom: IEEE, Aug. 2019, pp. 1–7

    work page 2019

  18. [18]

    A Hybrid Approach to Indoor Social Navigation: Integrating Reactive Local Planning and Proactive Global Planning,

    A. Debnath, G. J. Stein, and J. Ko ˇseck´a, “A Hybrid Approach to Indoor Social Navigation: Integrating Reactive Local Planning and Proactive Global Planning,” in2025 IEEE International Conference on Robotics and Automation (ICRA). Atlanta, GA, USA: IEEE, May 2025, pp. 10 432–10 438

    work page 2025

  19. [19]

    Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs,

    T. Ni, B. Eysenbach, and R. Salakhutdinov, “Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs,” inProceedings of the 39th International Conference on Machine Learning. PMLR, Jun. 2022, pp. 16 691–16 723

    work page 2022

  20. [20]

    V oronoi diagrams—a survey of a fundamental ge- ometric data structure,

    F. Aurenhammer, “V oronoi diagrams—a survey of a fundamental ge- ometric data structure,”ACM Computing Surveys, vol. 23, no. 3, pp. 345–405, Sep. 1991

    work page 1991

  21. [21]

    Proximal Policy Optimization Algorithms,

    J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal Policy Optimization Algorithms,” Aug. 2017

    work page 2017

  22. [22]

    IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures,

    L. Espeholt, H. Soyer, R. Munos, K. Simonyan, V . Mnih, T. Ward, Y . Doron, V . Firoiu, T. Harley, I. Dunning, S. Legg, and K. Kavukcuoglu, “IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures,” inProceedings of the 35th International Conference on Machine Learning. PMLR, Jul. 2018, pp. 1407–1416

    work page 2018

  23. [23]

    Optuna: A Next-generation Hyperparameter Optimization Framework,

    T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyama, “Optuna: A Next-generation Hyperparameter Optimization Framework,” inProceed- ings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Anchorage AK USA: ACM, Jul. 2019, pp. 2623–2631

    work page 2019

  24. [24]

    SLAM Toolbox: SLAM for the dynamic world,

    S. Macenski and I. Jambrecic, “SLAM Toolbox: SLAM for the dynamic world,”Journal of Open Source Software, vol. 6, no. 61, p. 2783, May 2021

    work page 2021

  25. [25]

    Adapting the Sample Size in Particle Filters Through KLD- Sampling,

    D. Fox, “Adapting the Sample Size in Particle Filters Through KLD- Sampling,”The International Journal of Robotics Research, vol. 22, no. 12, pp. 985–1003, Dec. 2003

    work page 2003

  26. [26]

    Robot Operating System 2: Design, architecture, and uses in the wild,

    S. Macenski, T. Foote, B. Gerkey, C. Lalancette, and W. Woodall, “Robot Operating System 2: Design, architecture, and uses in the wild,”Science Robotics, vol. 7, no. 66, p. eabm6074, May 2022

    work page 2022