4 accepted papers
This paper develops LongNav-R1, an end-to-end multi-turn reinforcement learning (RL) framework designed to optimize Visual-Language-Action (VLA) models for long-horizon navigation. Unlike existing single-turn paradigm, LongNav-R1 reformulates the navigation decision process as a continuous multi-tur…
Uncertainty quantification is crucial for autonomous systems, enabling safe and robust decision making in tasks ranging from active perception to robotic planning. This paper introduces a novel approach to quantify uncertainty for radiance fields by deriving pixel-wise moment expressions from the re…
Safe motion planning algorithms are necessary for deploying autonomous robots in unstructured environments to prevent harm to humans and avoid damage to nearby objects. Generating these motion plans in real-time is also important to ensure that the robot can adapt to sudden changes in its environmen
This letter proposes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LONER</i> , the first real-time LiDAR SLAM algorithm that uses a neural implicit scene representation. Existing implicit mapping methods for LiDAR show promising results in large-sc