Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Zhi, Weiming"'
Autor:
Zheng, Jiaxi, Dai, Guangmin, He, Botao, Mu, Zhaoyang, Meng, Zhaochen, Zhang, Tianyi, Zhi, Weiming, Fan, Dixia
This paper presents a low-cost, centralized modular underwater robot platform, ModCube, which can be used to study swarm coordination for a wide range of tasks in underwater environments. A ModCube structure consists of multiple ModCube robots. Each
Externí odkaz:
http://arxiv.org/abs/2409.15627
Autor:
Zhi, Weiming
Contemporary robots have become exceptionally skilled at achieving specific tasks in structured environments. However, they often fail when faced with the limitless permutations of real-world unstructured environments. This motivates robotics methods
Externí odkaz:
http://arxiv.org/abs/2407.10383
Humans have the remarkable ability to use held objects as tools to interact with their environment. For this to occur, humans internally estimate how hand movements affect the object's movement. We wish to endow robots with this capability. We contri
Externí odkaz:
http://arxiv.org/abs/2407.10331
Autor:
Zhang, Tianyi, Zhi, Weiming, Huang, Kaining, Mangelson, Joshua, Barbalata, Corina, Johnson-Roberson, Matthew
Water caustics are commonly observed in seafloor imaging data from shallow-water areas. Traditional methods that remove caustic patterns from images often rely on 2D filtering or pre-training on an annotated dataset, hindering the performance when ge
Externí odkaz:
http://arxiv.org/abs/2407.10318
Representing the environment is a central challenge in robotics, and is essential for effective decision-making. Traditionally, before capturing images with a manipulator-mounted camera, users need to calibrate the camera using a specific external ma
Externí odkaz:
http://arxiv.org/abs/2404.11683
This paper introduces Spatial Diagrammatic Instructions (SDIs), an approach for human operators to specify objectives and constraints that are related to spatial regions in the working environment. Human operators are enabled to sketch out regions di
Externí odkaz:
http://arxiv.org/abs/2403.12465
DarkGS: Learning Neural Illumination and 3D Gaussians Relighting for Robotic Exploration in the Dark
Publikováno v:
IEEE/RSJ International Conference on Intelligent Robots and Systems 2024
Humans have the remarkable ability to construct consistent mental models of an environment, even under limited or varying levels of illumination. We wish to endow robots with this same capability. In this paper, we tackle the challenge of constructin
Externí odkaz:
http://arxiv.org/abs/2403.10814
The ability to construct concise scene representations from sensor input is central to the field of robotics. This paper addresses the problem of robustly creating a 3D representation of a tabletop scene from a segmented RGB-D image. These representa
Externí odkaz:
http://arxiv.org/abs/2403.08106
Diagrammatic Teaching is a paradigm for robots to acquire novel skills, whereby the user provides 2D sketches over images of the scene to shape the robot's motion. In this work, we tackle the problem of teaching a robot to approach a surface and then
Externí odkaz:
http://arxiv.org/abs/2309.10298
Pedestrian trajectory prediction plays an important role in autonomous driving systems and robotics. Recent work utilizing prominent deep learning models for pedestrian motion prediction makes limited a priori assumptions about human movements, resul
Externí odkaz:
http://arxiv.org/abs/2309.09021