Tootfinder

A Demonstration of Interstellar Navigation Using #NewHorizons: https://iopscience.iop.org/article/10.3847/1538-3881/addabe -> Spacecraft can navigate using light from just two stars: https://physicsworld.com/a/spacecraft-can-navigate-using-light-from-just-two-stars/

Netscape Navigator 2.0 was released 30 years ago today [jwz]
#internet

Manipulate-to-Navigate: Reinforcement Learning with Visual Affordances and Manipulability Priors
Yuying Zhang, Joni Pajarinen
https://arxiv.org/abs/2508.13151 https://

Manipulate-to-Navigate: Reinforcement Learning with Visual Affordances and Manipulability Priors
Mobile manipulation in dynamic environments is challenging due to movable obstacles blocking the robot's path. Traditional methods, which treat navigation and manipulation as separate tasks, often fail in such 'manipulate-to-navigate' scenarios, as obstacles must be removed before navigation. In these cases, active interaction with the environment is required to clear obstacles while ensuring sufficient space for movement. To address the manipulate-to-navigate problem, we propose a reinforcemen…

19.000 Kilometer dichter dran: US-Startup will GPS-Alternative liefern
Das US-Startup Xona Space Systems plant eine erdnahe GPS-Alternative. 258 Satelliten sollen präzisere und störungsresistentere Navigationsdaten liefern.

EEG-Based Cognitive Load Classification During Landmark-Based VR Navigation
Jiahui An, Bingjie Cheng, Dmitriy Rudyka, Elisa Donati, Sara Fabrikant
https://arxiv.org/abs/2509.14056

EEG-Based Cognitive Load Classification During Landmark-Based VR Navigation
Brain computer interfaces enable real-time monitoring of cognitive load, but their effectiveness in dynamic navigation contexts is not well established. Using an existing VR navigation dataset, we examined whether EEG signals can classify cognitive load during map-based wayfinding and whether classification accuracy depends more on task complexity or on individual traits. EEG recordings from forty-six participants navigating routes with 3, 5, or 7 map landmarks were analyzed with a nested cross…

Reinforcement Learning for Autonomous Point-to-Point UAV Navigation
Salim Oyinlola, Nitesh Subedi, Soumik Sarkar
https://arxiv.org/abs/2509.13943 https://a…

Reinforcement Learning for Autonomous Point-to-Point UAV Navigation
Unmanned Aerial Vehicles (UAVs) are increasingly used in automated inspection, delivery, and navigation tasks that require reliable autonomy. This project develops a reinforcement learning (RL) approach to enable a single UAV to autonomously navigate between predefined points without manual intervention. The drone learns navigation policies through trial-and-error interaction, using a custom reward function that encourages goal-reaching efficiency while penalizing collisions and unsafe behavior…

FFI-VTR: Lightweight and Robust Visual Teach and Repeat Navigation based on Feature Flow Indicator and Probabilistic Motion Planning
Jikai Wang, Yunqi Cheng, Zonghai Chen
https://arxiv.org/abs/2507.12800

FFI-VTR: Lightweight and Robust Visual Teach and Repeat Navigation based on Feature Flow Indicator and Probabilistic Motion Planning
Though visual and repeat navigation is a convenient solution for mobile robot self-navigation, achieving balance between efficiency and robustness in task environment still remains challenges. In this paper, we propose a novel visual and repeat robotic autonomous navigation method that requires no accurate localization and dense reconstruction modules, which makes our system featured by lightweight and robustness. Firstly, feature flow is introduced and we develop a qualitative mapping between …

i2Nav-Robot: A Large-Scale Indoor-Outdoor Robot Dataset for Multi-Sensor Fusion Navigation and Mapping
Hailiang Tang, Tisheng Zhang, Liqiang Wang, Xin Ding, Man Yuan, Zhiyu Xiang, Jujin Chen, Yuhan Bian, Shuangyan Liu, Yuqing Wang, Guan Wang, Xiaoji Niu
https://arxiv.org/abs/2508.11485

i2Nav-Robot: A Large-Scale Indoor-Outdoor Robot Dataset for Multi-Sensor Fusion Navigation and Mapping
Accurate and reliable navigation is crucial for autonomous unmanned ground vehicle (UGV). However, current UGV datasets fall short in meeting the demands for advancing navigation and mapping techniques due to limitations in sensor configuration, time synchronization, ground truth, and scenario diversity. To address these challenges, we present i2Nav-Robot, a large-scale dataset designed for multi-sensor fusion navigation and mapping in indoor-outdoor environments. We integrate multi-modal senso…

MetricNet: Recovering Metric Scale in Generative Navigation Policies
Abhijeet Nayak, D\'ebora N. P. Oliveira, Samiran Gode, Cordelia Schmid, Wolfram Burgard
https://arxiv.org/abs/2509.13965

MetricNet: Recovering Metric Scale in Generative Navigation Policies
Generative navigation policies have made rapid progress in improving end-to-end learned navigation. Despite their promising results, this paradigm has two structural problems. First, the sampled trajectories exist in an abstract, unscaled space without metric grounding. Second, the control strategy discards the full path, instead moving directly towards a single waypoint. This leads to short-sighted and unsafe actions, moving the robot towards obstacles that a complete and correctly scaled path…

Agile in the Face of Delay: Asynchronous End-to-End Learning for Real-World Aerial Navigation
Yude Li, Zhexuan Zhou, Huizhe Li, Youmin Gong, Jie Mei
https://arxiv.org/abs/2509.13816

Agile in the Face of Delay: Asynchronous End-to-End Learning for Real-World Aerial Navigation
Robust autonomous navigation for Autonomous Aerial Vehicles (AAVs) in complex environments is a critical capability. However, modern end-to-end navigation faces a key challenge: the high-frequency control loop needed for agile flight conflicts with low-frequency perception streams, which are limited by sensor update rates and significant computational cost. This mismatch forces conventional synchronous models into undesirably low control rates. To resolve this, we propose an asynchronous reinfo…