no code implementations • 12 Mar 2024 • Di Kevin Gao, Andrew Haverly, Sudip Mittal, Jiming Wu, Jingdao Chen
Artificial intelligence (AI) ethics has emerged as a burgeoning yet pivotal area of scholarly research.
no code implementations • 12 Oct 2023 • Subash Neupane, Shaswata Mitra, Ivan A. Fernandez, Swayamjit Saha, Sudip Mittal, Jingdao Chen, Nisha Pillai, Shahram Rahimi
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.
1 code implementation • 15 Sep 2023 • Charles Moore, Shaswata Mitra, Nisha Pillai, Marc Moore, Sudip Mittal, Cindy Bethel, Jingdao Chen
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.
no code implementations • 28 Jun 2023 • Jinhee Yu, Jingdao Chen, Lalitha Dabbiru, Christopher T. Goodin
In the absence of spatial domain changes between training and testing, models trained and tested on the same sensor type generally exhibited better performance.
no code implementations • 15 May 2023 • Seongyong Kim, Yosuke Yajima, Jisoo Park, Jingdao Chen, Yong K. Cho
The clutter points are removed whereas the wall, door, and stair points are used for 2D floorplan generation.
no code implementations • 15 Feb 2023 • Sudip Mittal, Jingdao Chen
Robotics, automation, and related Artificial Intelligence (AI) systems have become pervasive bringing in concerns related to security, safety, accuracy, and trust.
no code implementations • 17 Oct 2022 • Isaac Ronald Ward, Charles Moore, Kai Pak, Jingdao Chen, Edwin Goh
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.
no code implementations • 27 Sep 2022 • Grace Vincent, Alice Yepremyan, Jingdao Chen, Edwin Goh
Planetary rover missions must utilize machine learning-based perception to continue extra-terrestrial exploration with little to no human presence.
no code implementations • 1 Feb 2022 • Edwin Goh, Jingdao Chen, Brian Wilson
Planetary rover systems need to perform terrain segmentation to identify drivable areas as well as identify specific types of soil for sample collection.
1 code implementation • 16 Mar 2021 • Jingdao Chen, Zsolt Kira, Yong K. Cho
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.
1 code implementation • 18 Feb 2019 • Jingdao Chen, Yong K. Cho, Zsolt Kira
Mobile robots need to create high-definition 3D maps of the environment for applications such as remote surveillance and infrastructure mapping.
Robotics