MazePlanning Benchmark

Updated 30 October 2025

Maze planning benchmarks are standardized frameworks that evaluate planning algorithms in maze-like environments with complex obstacles.
They integrate diverse robot models and algorithm classes, enabling rigorous comparisons across classical, sampling-based, and learning-based methods.
They employ procedural generation and unified evaluation metrics to ensure reproducibility, fairness, and statistically significant performance insights.

Maze planning benchmarks are standardized frameworks designed to evaluate, compare, and analyze the performance of planning algorithms—both classical and learning-based—in environments characterized by complex obstacle layouts and constrained navigational tasks. These environments, commonly termed "maze-like," are essential for stress-testing the capabilities of planners with respect to efficiency, completeness, generalization, and robustness under realistic settings such as robotic manipulation, autonomous navigation, and embodied agent interaction.

1. Benchmark Objectives and Significance

Maze planning benchmarks aim to address several limitations in the evaluation of motion and path planning algorithms. Key goals include reproducibility of results, fairness in comparison, statistical significance, extensibility to new problems, and robust evaluation under environment and robot diversity.

Benchmarks such as MotionBenchMaker (Chamzas et al., 2021), PathBench (Hsueh et al., 2022, Toma et al., 2021), OMPL (Moll et al., 2014), and others provide large, procedurally generated problem sets, standardized metrics, and unified toolchains for automated batch evaluation. This minimizes human bias, enables broad generalization, and facilitates direct comparison of algorithms that may otherwise be assessed only on small, idiosyncratic, or hand-crafted sets of maze problems.

Benchmarking in maze-like settings is integral to evaluating planners on narrow passages, complex homotopy classes, and adversarial map structures, all of which are challenging cases in both manipulation and mobile robotics.

2. Supported Robots, Algorithms, and Environments

Modern maze planning benchmarks support a wide array of robots, algorithms, and environmental settings:

Robot Types: High-degree-of-freedom (DOF) manipulators (Fetch, Panda, UR5, Baxter, KUKA+ShadowHand, 31-DOF systems) (Chamzas et al., 2021), wheeled mobile robots (realistic nonholonomic models with Dubins/Reeds-Shepp/CC steer functions) (Heiden et al., 2020), and simplified agents for grid worlds and 2D navigation (Hsueh et al., 2022, Toma et al., 2021).
Algorithm Classes:
- Classical: Graph-based (A*, Dijkstra, wavefront), sampling-based (RRT, PRM, BIT*, Informed RRT*), state lattice, any-angle, and kinodynamic planners.
- Learning-based: Value Iteration Networks, Gated Path Planning Networks, Motion Planning Networks, LSTM-based policies, and end-to-end RL agents (Toma et al., 2021, Sukhbaatar et al., 2015).
- Non-learning, parameter-free: Max-pooling-based architectures (oMAP) for large-scale maze propagation (Kulvicius et al., 2020).
- Deep learning combinatorial: Recurrent ConvNet-based RCNN solutions for multi-terminal Steiner tree problems as maze-solving tasks (Ramos et al., 24 Oct 2024).
- Local planners/motion optimizers: Model Predictive Control, Optimization Fabrics, local reactive planners for constrained spaces (Spahn et al., 2022).
Environments: Benchmarks include procedurally generated scenes (shelves, tables, cabinets, block maps, house maps, adversarial passages), real-world maps derived from SLAM or video games, and fully synthetic random obstacle layouts. Dataset suites often comprise hundreds or thousands of unique maze instances per robot/environment pairing (Chamzas et al., 2021, Hsueh et al., 2022).

3. Dataset Generation and Diversity

State-of-the-art benchmarks such as MotionBenchMaker (Chamzas et al., 2021) employ procedural generation techniques for maze environments:

Scene Sampling: Object pose perturbation using Gaussian/Uniform distributions; kinematically constrained sampling via URDFs.
Octomap Generation: Conversion from geometric scenes to "sensed" point cloud or octomap representations, emulating realistic sensor noise and partial observability.
Problem Definition: Start/goal states are specified as robot-agnostic manipulation queries, internalized by collision-aware IK or sample-based planners.
Multi-DOF and Multi-effector: Support for bimanual and dexterous manipulation queries; multi-tip and multi-end-effector scenarios.
Manifest and Metadata: Each dataset is accompanied by descriptive files, which specify generation parameters and facilitate reproducibility.

For grid-based and pathfinding benchmarks, built-in generators produce uniform random fill, block maps, house/floorplan layouts, complex intersections/traps, and point cloud encodings (Hsueh et al., 2022, Toma et al., 2021, Godin-Dubois et al., 20 Nov 2024).

The diversity and automatic generation of large problem sets are critical for statistically significant comparisons—small, hand-curated sets are demonstrably susceptible to sampling bias leading to misleading conclusions (see analysis in (Chamzas et al., 2021), Figures 4/5).

4. Benchmarking Methodologies and Performance Metrics

Maze planning benchmarks implement robust methodologies and evaluation protocols:

Scriptable Interfaces: Unified APIs for loading datasets, configuring planners, and batch evaluation. Compatibility with ROS (Robot Operating System), visualization toolchains (Robowflex, Panda3D), and plotting utilities (PlannerArena) allow for cross-standard analyses (Chamzas et al., 2021, Hsueh et al., 2022).
Metrics:
- Success Rate: Fraction of problems solved.
- Path Length / Solution Quality: Aggregate joint path length, deviation from optimal (A* reference), smoothness, curvature, and number of cusps (Heiden et al., 2020, Hsueh et al., 2022).
- Computational Time: Wall-clock runtime per instance.
- Obstacle Clearance: Mean minimum distance from path to obstacles.
- Explored Search Space: Map coverage during planning.
- Resource Consumption: Memory usage.
- Step Efficiency: Steps/optimal steps, backtracking rates (especially in LLM decision-making) (Einarsson, 27 Jul 2025).
Statistical Aggregation: Mean, median, confidence bounds; log-scaled visualizations to elucidate runtime and solution variance.
Parameter Sweeps and Sensitivity: Systematic variation of planner parameters to identify optimal and sensitive regimes.

Key formulas include normalized path cost: $Cost = \sum_{i=1}^{N-1} \| \mathbf{q}_{i+1} - \mathbf{q}_i \|$ for joint trajectory evaluations (Chamzas et al., 2021), and empirical path deviation: $\text{Deviation (\%)} = \frac{L_{\text{alg}} - L^*}{L^*} \times 100$ where $L_{\text{alg}}$ is computed path length, and $L^*$ is optimal (Hsueh et al., 2022, Toma et al., 2021).

5. Comparison and Analysis of Planner Classes

Benchmark quantitative results consistently reveal that:

No planner dominates universally: Performance varies with robot DOF, map complexity, and parameter tuning (Chamzas et al., 2021).
Classical planners (A*, Dijkstra) produce deterministic, optimal solutions in grid settings, but may not generalize to high-DOF, nonholonomic, or sensor-derived maps.
Sampling-based planners excel in high-DOF, continuous spaces and can be optimized via parameter sweeps but may suffer on narrow passages unless carefully tuned and post-smoothed (Heiden et al., 2020).
Learning-based methods show strong performance in environments matching their training distribution; generalization is improved with curriculum learning or interactive (human-in-the-loop) training regimes (Godin-Dubois et al., 20 Nov 2024, Sukhbaatar et al., 2015).
Unsupervised analyzer architectures (oMAP) enable highly scalable and deterministic maze propagation on massive grids, offering optimal BFS-equivalent distances with GPU-level parallelism and no need for training (Kulvicius et al., 2020).
Image-based deep learning solutions (MazeNet): By treating combinatorial tasks as maze-solving in image space, RCNNs deliver state-of-the-art accuracy and runtime scaling for multi-terminal obstacle-avoiding problems (Ramos et al., 24 Oct 2024).

A summary comparison of planners on representative metrics (extracted from (Hsueh et al., 2022, Chamzas et al., 2021)):

Planner	Path Deviation (%)	Success Rate (%)	Computation Time (s)
A*	0.00	99.4	0.106
d-RRT	19.91	96.8	7.336
VIN	5.25	18.2	1.796
WPN	2.78	99.4	0.817

Some planners (e.g., MazeNet) achieve perfect solution accuracy and scale linearly with maze size/terminal count, outperforming classical and approximate combinatorial algorithms for specific maze problems (Ramos et al., 24 Oct 2024).

6. Reproducibility, Fairness, and Extensibility

Leading benchmarks incorporate concrete mechanisms for reproducibility, fairness, and extensibility:

Config-anchored problem generation: All parameters, random seeds, and scene setups are stored and can be replayed (Chamzas et al., 2021, Moll et al., 2014).
Open-source datasets and manifests: Full problem sets (start/goal, robot models, obstacle maps) are shared in public repositories.
Unified logging and database schemas: Standardized result formats (e.g., OMPL log/SQLite3) facilitate cross-library/cross-lab comparison (Moll et al., 2014).
Modular design: New algorithms or metrics can be integrated using clear APIs or plugin wrappers (Spahn et al., 2022, Hsueh et al., 2022).
Automated batch checking: Feasibility checking, multi-run statistical aggregation, and fairness protocols ensure robust results.

Procedural diversity intrinsically counteracts overfitting and sampling bias, exposing planner strengths/weaknesses that standard hand-crafted sets obscure.

7. Future Directions and Benchmark Evolution

Recent advances highlight several future trajectories for maze planning benchmarks:

Partial observability and dynamic environments: Benchmarks such as EvoEmpirBench introduce locally observable, dynamic mazes with environmental evolution, adversarial hazards, and adaptive multi-objective tasks (Zhao et al., 16 Sep 2025).
Evaluation of spatial reasoning in LLM-based agents: By stripping visual/contextual cues and isolating coordinate-based navigation, benchmarks like MazeEval identify key limitations in agentic reasoning, efficiency, and language transfer (Einarsson, 27 Jul 2025).
Scenario-driven, human-realist tasks: Memory-Maze simulates visually-impaired user navigation, exposing real-world instruction ambiguities and testing robustness of current VLN models (Kuribayashi et al., 11 May 2024).
Generalization metrics and human-in-the-loop protocols: AMaze demonstrates that scaffolded and interactive training can dramatically boost agent generalization to unseen maze configurations, beyond what single-environment benchmarks afford (Godin-Dubois et al., 20 Nov 2024).

A plausible direction for the field is a convergence between large-scale automatic generation of task diversity, language-enabled spatial tasks, sensorimotor realism, and cross-domain benchmarking, providing comprehensive assessment across the dimensions of efficiency, adaptability, and embodied generalization.

References

MotionBenchMaker: (Chamzas et al., 2021)
MazeNet: (Ramos et al., 24 Oct 2024)
oMAP: (Kulvicius et al., 2020)
PathBench: (Hsueh et al., 2022, Toma et al., 2021)
OMPL Benchmark Infrastructure: (Moll et al., 2014)
AMaze: (Godin-Dubois et al., 20 Nov 2024)
LocalPlannerBench: (Spahn et al., 2022)
MazeBase: (Sukhbaatar et al., 2015)
BenchNav: (Endo et al., 22 May 2024)
Memory-Maze: (Kuribayashi et al., 11 May 2024)
Global Motion Planning for Wheeled Robots: (Heiden et al., 2020)
EvoEmpirBench: (Zhao et al., 16 Sep 2025)
MazeEval: (Einarsson, 27 Jul 2025)

Maze planning benchmarks are foundational to rigorous, unbiased, and reproducible evaluation of planning systems, and ongoing advances continue to extend their capability and impact across robotics, AI, and embodied agent research.