RVN-Bench: A Benchmark for Reactive Visual Navigation

Jaewon Lee; Jaeseok Heo; Gunmin Lee; Howoong Jun; Jeongwoo Oh; Songhwai Oh

cs.ROMar 4, 2026

RVN-Bench: A Benchmark for Reactive Visual Navigation

Authors: Jaewon Lee, Jaeseok Heo, Gunmin Lee, Howoong Jun, Jeongwoo Oh, Songhwai Oh

Organizations: Department of Electrical and Computer Engineering, Seoul National University (SNU) and Automation and Systems Research Institute (ASRI) and Sequor Robotics Inc., Seoul, Korea (Republic of). · Department of Electrical and Computer Engineering, Seoul National University (SNU) and Automation and Systems Research Institute (ASRI), Seoul, Korea (Republic of). · Interdisciplinary Program in Artificial Intelligence, Seoul National University (SNU) and Automation and Systems Research Institute (ASRI) and Sequor Robotics Inc., Seoul, Korea (Republic of). · Sequor Robotics Inc., Seoul, Korea (Republic of).

Abstract

Safe visual navigation is critical for indoor mobile robots operating in cluttered environments. Existing benchmarks, however, often neglect collisions or are designed for outdoor scenarios, making them unsuitable for indoor visual navigation. To address this limitation, we introduce the reactive visual navigation benchmark (RVN-Bench), a collision-aware benchmark for indoor mobile robots. In RVN-Bench, an agent must reach sequential goal positions in previously unseen environments using only visual observations and no prior map, while avoiding collisions. Built on the Habitat 2.0 simulator and leveraging high-fidelity HM3D scenes, RVN-Bench provides large-scale, diverse indoor environments, defines a collision-aware navigation task and evaluation metrics, and offers tools for standardized training and benchmarking. RVN-Bench supports both online and offline learning by offering an environment for online reinforcement learning, a trajectory image dataset generator, and tools for producing negative trajectory image datasets that capture collision events. Evaluations demonstrate that policies trained on RVN-Bench generalize effectively across unseen simulated environments. Furthermore, initial physical experiments using a Jackal UGV indicate promising sim-to-real transfer. Code and additional materials are available at: https://sequor-robotics-research.github.io/projects/RVN-Bench/.

Figures & tables

Fig. 1: Overview of the RVN-Bench. The benchmark is designed for indoor mobile robots and focuses on collision-aware visual navigation, where an agent must reach sequential goal positions using only visual observations while avoiding collisions. Built upon Habitat 2.0 and utilizing HM3D scenes, it provides high-quality visual observations, an RL environment for training and evaluation, and pipelines for collecting trajectory image datasets, including negative (collision-inducing) trajectories.

Benchmark	Goal Type	Domain	Realistic Visual Rendering	Detect Collision	Lifelong Evaluation	Dynamic Obstacles
CARLA [ 5 ]	High-level command	Autonomous driving	✓	✓		✓
MetaUrban [ 13 ]	Point	Outdoor		✓		✓
Habitat Challenge [ 7 , 18 ]	Point, object, image	Indoor	✓
HM3D-OVON [ 19 ]	Language	Indoor	✓
GOAT-Bench [ 11 ]	Object, image, language	Indoor	✓		✓
HabiCrowd [ 10 ]	Point, object	Indoor	✓	▲		✓

TABLE I: Comparison of Visual Navigation Benchmarks.

Fig. 2: Trajectory image dataset generation process. We generate trajectory image datasets through the following steps. (a) Start and goal positions are randomly sampled in the scene. (b) The shortest path between these positions is computed using the ground truth occupancy map padded with a margin larger than the agent’s radius. (c) The agent follows this path using discrete actions. (d) If the agent reaches the goal position without collision, its positions, yaws, and image observations at each time step are recorded. For negative trajectory image dataset generation , the occupancy map is padded with a margin smaller than the agent radius to create path that is unsafe for the agent in step (b) . If the agent collides with an obstacle at $t=t_{c}$ , the history of its positions, yaws, and image observations from $t_{i}$ to $t_{f}$ is recorded, where $t_{i}=\max(0,t_{c}-k_{\text{pre}})$ , $t_{f}=\min(T,t_{c}+k_{\text{post}})$ , and $I_{t}$ is set to $I_{t_{c}}\text{for }t>t_{c}$ .

Category	Method	Train			Validation			Test
Category	Method	SR 1 $\uparrow$	$E(G)$ $\uparrow$	CPK $\downarrow$	SR 1 $\uparrow$	$E(G)$ $\uparrow$	CPK $\downarrow$	SR 1 $\uparrow$	$E(G)$ $\uparrow$	CPK $\downarrow$
IL	ViNT-PointGoal [ 2 ]	0.097	0.11	442.3	0.090	0.11	468.3	0.093	0.10	465.4
	NoMaD-PointGoal [ 3 ]	0.758	4.01	32.5	0.752	3.69	35.7	0.751	4.52	31.0
	NoMaD-Neg	0.750	3.83	31.2	0.757	3.97	28.6	0.760	4.61	25.8
Safe-RL	PPO-Lagrangian [ 17 ]	0.826	7.88	15.7	0.803	7.61	16.9	0.805	9.02	13.7
RL	PPO [ 16 ]	0.824	8.70	15.6	0.808	8.54	16.4	0.819	8.68	15.5

TABLE II: RVN-Bench Results.

Fig. 3: Overview of the NoMaD-Neg framework. (a) Two NoMaD-PointGoal model, NoMaD e and NoMaD n , are trained separately with expert and negative trajectory datasets. (b) NoMaD e and NoMaD n predict expert trajectories $a_{e,i}\in\textbf{a}_{E}$ and negative trajectories $a_{n,i}\in\textbf{a}_{N}$ , respectively. The expert trajectory $a_{e}^{*}$ with minimal $\text{CoR}(a_{e,i},\textbf{a}_{E},\textbf{a}_{N})$ is then selected.

Method	Observation Type	Train			Validation			Test
Method	Observation Type	SR 1 $\uparrow$	$E(G)$ $\uparrow$	CPK $\downarrow$	SR 1 $\uparrow$	$E(G)$ $\uparrow$	CPK $\downarrow$	SR 1 $\uparrow$	$E(G)$ $\uparrow$	CPK $\downarrow$
DD-PPO [ 15 ]	RGB	0.899	13.28	9.2	0.865	11.86	10.5	0.886	13.90	8.7
	RGB + Predicted Depth [ 25 ]	0.937	22.70	3.5	0.909	19.88	4.0	0.928	20.79	3.6
	RGB + GT Depth	0.946	20.79	2.0	0.920	21.90	2.2	0.940	22.99	1.8

TABLE III: Ablation study on effect of observation types of DDPPO.

Fig. 4: Real-world evaluation of NoMaD-PointGoal with different training datasets. (a) Clearpath Jackal UGV platform [ 31 , 32 , 33 ] used in the experiments. (b) Illustration of evaluation results in a house environment. Shown are the executed trajectories of the NoMaD-PointGoal agent trained on the real-world dataset (pink), trained on the simulation dataset (yellow), and trained on the combined real-world and simulation datasets (blue).

Model	Dataset	SR 1 $\uparrow$	$E(G)$ $\uparrow$	CPK $\downarrow$
NoMaD-PointGoal [ 3 ]	Real	0.30	0.30	223.5
	Sim	0.60	1.05	196.4
	Real+Sim	0.75	1.30	191.4

TABLE IV: Real-world evaluation results of NoMaD-PointGoal models trained with different training datasets

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NavVerse: Benchmarking Indoor-to-Outdoor Embodied Navigation in Continuous Robot Simulation

Junzhe Wu, Yue Hu, Zeyu Han, Po-Hsun Chang+3

University of Michigan, Ann Arbor · Tsinghua University

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Robots deployed in delivery, campus, and emergency-response settings often need to navigate from buildings to streets within a single continuous episode. Existing benchmarks usually evaluate indoor and outdoor navigation separately, and many abstract away robot execution, leaving exit finding, boundary traversal, adaptation, and kinodynamic failures underexplored. We introduce NavVerse, a physics-enabled benchmark for indoor-to-outdoor embodied navigation. NavVerse contains 100 indoor scenes, 50 urban outdoor scenes, and 50 indoor-to-outdoor scenes, and 10,000 episodes spanning Object Navigation, Vision-and-Language Navigation, and Place Navigation tasks, where agents search for semantic points of interest such as restaurants or banks. Agents are evaluated through executable robot interfaces using task-success, path-efficiency, and safety metrics. Zero-shot experiments with RL, VLA, and modular baselines show that current agents remain far from solving cross-context navigation: end-to-end VLAs obtain the highest zero-shot success, while the modular method provides the strongest safety profile. PlaceNav further reveals a clear drop from outdoor to indoor-to-outdoor scenes, indicating that adaptation remains major bottleneck.

Abstract

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