[2603.03953] RVN-Bench: A Benchmark for Reactive Visual Navigation

Computer Science > Robotics arXiv:2603.03953 (cs) [Submitted on 4 Mar 2026] Title:RVN-Bench: A Benchmark for Reactive Visual Navigation Authors:Jaewon Lee, Jaeseok Heo, Gunmin Lee, Howoong Jun, Jeongwoo Oh, Songhwai Oh View a PDF of the paper titled RVN-Bench: A Benchmark for Reactive Visual Navigation, by Jaewon Lee and 4 other authors View PDF HTML (experimental) 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. Experiments show that policies trained on RVN-Benc...