惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
U
Unit 42
IT之家
IT之家
Y
Y Combinator Blog
T
Tailwind CSS Blog
B
Blog
大猫的无限游戏
大猫的无限游戏
博客园 - 叶小钗
Jina AI
Jina AI
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
I
InfoQ
J
Java Code Geeks
F
Fortinet All Blogs
T
The Blog of Author Tim Ferriss
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
H
Hackread – Cybersecurity News, Data Breaches, AI and More
人人都是产品经理
人人都是产品经理
腾讯CDC
Hugging Face - Blog
Hugging Face - Blog
GbyAI
GbyAI
博客园 - 司徒正美
The GitHub Blog
The GitHub Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
L
LangChain Blog

cs.RO updates on arXiv.org

FineCog-Nav: Integrating Fine-grained Cognitive Modules for Zero-shot Multimodal UAV Navigation DENALI: A Dataset Enabling Non-Line-of-Sight Spatial Reasoning with Low-Cost LiDARs SENSE: Stereo OpEN Vocabulary SEmantic Segmentation Continual Hand-Eye Calibration for Open-world Robotic Manipulation PLAF: Pixel-wise Language-Aligned Feature Extraction for Efficient 3D Scene Understanding GaussianFlow SLAM: Monocular Gaussian Splatting SLAM Guided by GaussianFlow GIST: Multimodal Knowledge Extraction and Spatial Grounding via Intelligent Semantic Topology $π_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities R3D: Revisiting 3D Policy Learning Vision-Based Safe Human-Robot Collaboration with Uncertainty Guarantees Benchmarking Classical Coverage Path Planning Heuristics on Irregular Hexagonal Grids for Maritime Coverage Scenarios NEAT-NC: NEAT guided Navigation Cells for Robot Path Planning HRDexDB: A Large-Scale Dataset of Dexterous Human and Robotic Hand Grasps ADAPT: Benchmarking Commonsense Planning under Unspecified Affordance Constraints An Intelligent Robotic and Bio-Digestor Framework for Smart Waste Management Efficient closed-form approaches for pose estimation using Sylvester forms World-Value-Action Model: Implicit Planning for Vision-Language-Action Systems A Nonasymptotic Theory of Gain-Dependent Error Dynamics in Behavior Cloning CooperDrive: Enhancing Driving Decisions Through Cooperative Perception SpaceMind: A Modular and Self-Evolving Embodied Vision-Language Agent Framework for Autonomous On-orbit Servicing HiVLA: A Visual-Grounded-Centric Hierarchical Embodied Manipulation System UMI-3D: Extending Universal Manipulation Interface from Vision-Limited to 3D Spatial Perception Towards Multi-Object-Tracking with Radar on a Fast Moving Vehicle: On the Potential of Processing Radar in the Frequency Domain Beyond Conservative Automated Driving in Multi-Agent Scenarios via Coupled Model Predictive Control and Deep Reinforcement Learning Failure Identification in Imitation Learning Via Statistical and Semantic Filtering A Dynamic-Growing Fuzzy-Neuro Controller, Application to a 3PSP Parallel Robot Vision-Language-Action Jump-Starting for Reinforcement Learning Robotic Agents A Mechanistic Analysis of Sim-and-Real Co-Training in Generative Robot Policies ESCAPE: Episodic Spatial Memory and Adaptive Execution Policy for Long-Horizon Mobile Manipulation Evolvable Embodied Agent for Robotic Manipulation via Long Short-Term Reflection and Optimization
MRNaB: Mixed Reality-based Robot Navigation Interface usi...
Eduardo Iglesius, Masato Kobayashi, Yuki Uranishi, Haruo Takemur · 2024-03-28 · via cs.RO updates on arXiv.org

Recent advancements in robotics have led to the development of numerous interfaces to enhance the intuitiveness of robot navigation. However, the reliance on traditional 2D displays imposes limitations on the simultaneous visualization of information. Mixed Reality (MR) technology addresses this issue by enhancing the dimensionality of information visualization, allowing users to perceive multiple pieces of information concurrently. This paper proposes the Mixed Reality-based Robot Navigation Interface using an Optical-see-through MR-beacons (MRNaB), a novel approach that uses MR-beacons created with an ``air tap'', situated in the real world. This beacon is persistent, enabling multi-destination visualization and functioning as a signal transmitter for robot navigation, eliminating the need for repeated navigation inputs. Our system is mainly constructed into four primary functions: ``Add'', ``Move'', ``Delete'', and ``Select''. These allow for the addition of MR-beacons, location movement, its deletion, and the selection of MR-beacons for navigation purposes, respectively. To validate the effectiveness, we conducted comprehensive experiments comparing MRNaB with traditional 2D navigation systems. The results show significant improvements in user performance, both objectively and subjectively, confirming that the MRNaB enhances navigation efficiency and user experience. For additional material, please check: https://mertcookimg.github.io/mrnab