Tootfinder

Residual gaze behavior during navigation in blindness and low vision https://arxiv.org/abs/2509.11530v1

Residual Gaze Behavior During Navigation in Blindness and Low Vision
Background: Outdoor navigation poses significant challenges for people with blindness or low vision, yet the role of gaze behavior in supporting mobility remains underexplored. Fully sighted individuals typically adopt consistent scanning strategies, whereas those with visual impairments rely on heterogeneous adaptations shaped by residual vision and experience. Methods: We conducted a comparative eye-tracking study of fully sighted, low vision, blind, and fully blind participants navigating …

A Universal Vehicle-Trailer Navigation System with Neural Kinematics and Online Residual Learning
Yanbo Chen, Yunzhe Tan, Yaojia Wang, Zhengzhe Xu, Junbo Tan, Xueqian Wang
https://arxiv.org/abs/2507.15607

A Universal Vehicle-Trailer Navigation System with Neural Kinematics and Online Residual Learning
Autonomous navigation of vehicle-trailer systems is crucial in environments like airports, supermarkets, and concert venues, where various types of trailers are needed to navigate with different payloads and conditions. However, accurately modeling such systems remains challenging, especially for trailers with castor wheels. In this work, we propose a novel universal vehicle-trailer navigation system that integrates a hybrid nominal kinematic model--combining classical nonholonomic constraints …

GPS Denied IBVS-Based Navigation and Collision Avoidance of UAV Using a Low-Cost RGB Camera
Xiaoyu Wang, Yan Rui Tan, William Leong, Sunan Huang, Rodney Teo, Cheng Xiang
https://arxiv.org/abs/2509.17435

GPS Denied IBVS-Based Navigation and Collision Avoidance of UAV Using a Low-Cost RGB Camera
This paper proposes an image-based visual servoing (IBVS) framework for UAV navigation and collision avoidance using only an RGB camera. While UAV navigation has been extensively studied, it remains challenging to apply IBVS in missions involving multiple visual targets and collision avoidance. The proposed method achieves navigation without explicit path planning, and collision avoidance is realized through AI-based monocular depth estimation from RGB images. Unlike approaches that rely on ste…

Sensing, Social, and Motion Intelligence in Embodied Navigation: A Comprehensive Survey
Chaoran Xiong, Yulong Huang, Fangwen Yu, Changhao Chen, Yue Wang, Songpengchen Xia, Ling Pei
https://arxiv.org/abs/2508.15354

Sensing, Social, and Motion Intelligence in Embodied Navigation: A Comprehensive Survey
Embodied navigation (EN) advances traditional navigation by enabling robots to perform complex egocentric tasks through sensing, social, and motion intelligence. In contrast to classic methodologies that rely on explicit localization and pre-defined maps, EN leverages egocentric perception and human-like interaction strategies. This survey introduces a comprehensive EN formulation structured into five stages: Transition, Observation, Fusion, Reward-policy construction, and Action (TOFRA). The T…

Subteaming and Adaptive Formation Control for Coordinated Multi-Robot Navigation
Zihao Deng, Peng Gao, Williard Joshua Jose, Maggie Wigness, John Rogers, Brian Reily, Christopher Reardon, Hao Zhang
https://arxiv.org/abs/2509.16412

Subteaming and Adaptive Formation Control for Coordinated Multi-Robot Navigation
Coordinated multi-robot navigation is essential for robots to operate as a team in diverse environments. During navigation, robot teams usually need to maintain specific formations, such as circular formations to protect human teammates at the center. However, in complex scenarios such as narrow corridors, rigidly preserving predefined formations can become infeasible. Therefore, robot teams must be capable of dynamically splitting into smaller subteams and adaptively controlling the subteams t…

FiLM-Nav: Efficient and Generalizable Navigation via VLM Fine-tuning
Naoki Yokoyama, Sehoon Ha
https://arxiv.org/abs/2509.16445 https://arxiv.org/pdf/2509.…

FiLM-Nav: Efficient and Generalizable Navigation via VLM Fine-tuning
Enabling robotic assistants to navigate complex environments and locate objects described in free-form language is a critical capability for real-world deployment. While foundation models, particularly Vision-Language Models (VLMs), offer powerful semantic understanding, effectively adapting their web-scale knowledge for embodied decision-making remains a key challenge. We present FiLM-Nav (Fine-tuned Language Model for Navigation), an approach that directly fine-tunes pre-trained VLM as the na…

VLM-Empowered Multi-Mode System for Efficient and Safe Planetary Navigation
Sinuo Cheng, Ruyi Zhou, Wenhao Feng, Huaiguang Yang, Haibo Gao, Zongquan Deng, Liang Ding
https://arxiv.org/abs/2506.16703

VLM-Empowered Multi-Mode System for Efficient and Safe Planetary Navigation
The increasingly complex and diverse planetary exploration environment requires more adaptable and flexible rover navigation strategy. In this study, we propose a VLM-empowered multi-mode system to achieve efficient while safe autonomous navigation for planetary rovers. Vision-Language Model (VLM) is used to parse scene information by image inputs to achieve a human-level understanding of terrain complexity. Based on the complexity classification, the system switches to the most suitable naviga…

Ratatouille: Imitation Learning Ingredients for Real-world Social Robot Navigation
James R. Han, Mithun Vanniasinghe, Hshmat Sahak, Nicholas Rhinehart, Timothy D. Barfoot
https://arxiv.org/abs/2509.17204

Ratatouille: Imitation Learning Ingredients for Real-world Social Robot Navigation
Scaling Reinforcement Learning to in-the-wild social robot navigation is both data-intensive and unsafe, since policies must learn through direct interaction and inevitably encounter collisions. Offline Imitation learning (IL) avoids these risks by collecting expert demonstrations safely, training entirely offline, and deploying policies zero-shot. However, we find that naively applying Behaviour Cloning (BC) to social navigation is insufficient; achieving strong performance requires careful ar…

X-Nav: Learning End-to-End Cross-Embodiment Navigation for Mobile Robots
Haitong Wang, Aaron Hao Tan, Angus Fung, Goldie Nejat
https://arxiv.org/abs/2507.14731

X-Nav: Learning End-to-End Cross-Embodiment Navigation for Mobile Robots
Existing navigation methods are primarily designed for specific robot embodiments, limiting their generalizability across diverse robot platforms. In this paper, we introduce X-Nav, a novel framework for end-to-end cross-embodiment navigation where a single unified policy can be deployed across various embodiments for both wheeled and quadrupedal robots. X-Nav consists of two learning stages: 1) multiple expert policies are trained using deep reinforcement learning with privileged observations …

History-Augmented Vision-Language Models for Frontier-Based Zero-Shot Object Navigation
Mobin Habibpour, Fatemeh Afghah
https://arxiv.org/abs/2506.16623 ht…

History-Augmented Vision-Language Models for Frontier-Based Zero-Shot Object Navigation
Object Goal Navigation (ObjectNav) challenges robots to find objects in unseen environments, demanding sophisticated reasoning. While Vision-Language Models (VLMs) show potential, current ObjectNav methods often employ them superficially, primarily using vision-language embeddings for object-scene similarity checks rather than leveraging deeper reasoning. This limits contextual understanding and leads to practical issues like repetitive navigation behaviors. This paper introduces a novel zero-s…