Indoor Robot Navigation

Latest papers

32 papers10 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

RVN-Bench: A Benchmark for Reactive Visual Navigation
Mar 4, 2026Jaewon Lee, Jaeseok Heo, Gunmin Lee +3Robot Navigation Benchmarks Robot Navigation
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). +1
CoReLIN: Constraint-based Reasoning for Zero-shot Lifelong Interactive Navigation
Feb 23, 2026Apoorva Vashisth, Manav Kulshrestha, Pranav Bakshi +3Long-Horizon Robotic Manipulation Large Language Model-Based Robot Planning
Purdue University · IIT Kharagpur · DEVCOM Army Research Lab +1

Mar 4, 2026J/K move · Enter open · S save

RVN-Bench: A Benchmark for Reactive Visual Navigation

Jaewon Lee, Jaeseok Heo, Gunmin Lee, Howoong Jun+2

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).+1

Robot Navigation Benchmarks Robot Navigation Visual Navigation Indoor Robot Navigation Safe Robot Navigation Robot Collision Avoidance

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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/.

Latest papers

RVN-Bench: A Benchmark for Reactive Visual Navigation

CoReLIN: Constraint-based Reasoning for Zero-shot Lifelong Interactive Navigation