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Found 11 papers, 3 papers with code
AI Ethics: A Bibliometric Analysis, Critical Issues, and Key Gaps
Artificial intelligence (AI) ethics has emerged as a burgeoning yet pivotal area of scholarly research.
Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities
Motivated by the need to address the security concerns in AI-Robotics systems, this paper presents a comprehensive survey and taxonomy across three dimensions: attack surfaces, ethical and legal concerns, and Human-Robot Interaction (HRI) security.
URA*: Uncertainty-aware Path Planning using Image-based Aerial-to-Ground Traversability Estimation for Off-road Environments
Results show that the proposed image segmentation and planning methods outperform conventional planning algorithms in terms of the quality and feasibility of the initial path, as well as the quality of replanned paths.
Analysis of LiDAR Configurations on Off-road Semantic Segmentation Performance
In the absence of spatial domain changes between training and testing, models trained and tested on the same sensor type generally exhibited better performance.
A Hybrid Semantic-Geometric Approach for Clutter-Resistant Floorplan Generation from Building Point Clouds
The clutter points are removed whereas the wall, door, and stair points are used for 2D floorplan generation.
AI Security Threats against Pervasive Robotic Systems: A Course for Next Generation Cybersecurity Workforce
Robotics, automation, and related Artificial Intelligence (AI) systems have become pervasive bringing in concerns related to security, safety, accuracy, and trust.
Improving Contrastive Learning on Visually Homogeneous Mars Rover Images
In this study, we propose two approaches to resolve this: 1) an unsupervised deep clustering step on the Mars datasets, which identifies clusters of images containing similar semantic content and corrects false negative errors during training, and 2) a simple approach which mixes data from different domains to increase visual diversity of the total training dataset.
Mixed-domain Training Improves Multi-Mission Terrain Segmentation
Planetary rover missions must utilize machine learning-based perception to continue extra-terrestrial exploration with little to no human presence.
Mars Terrain Segmentation with Less Labels
Planetary rover systems need to perform terrain segmentation to identify drivable areas as well as identify specific types of soil for sample collection.
LRGNet: Learnable Region Growing for Class-Agnostic Point Cloud Segmentation
3D point cloud segmentation is an important function that helps robots understand the layout of their surrounding environment and perform tasks such as grasping objects, avoiding obstacles, and finding landmarks.
Multi-view Incremental Segmentation of 3D Point Clouds for Mobile Robots
Mobile robots need to create high-definition 3D maps of the environment for applications such as remote surveillance and infrastructure mapping.
Robotics
