26 citations · 29 across the 26 of their papers we have counts for
10 papers · 1 filter
Cross-Modal Instructions for Robot Motion Generation
William Barron, Xiaoxiang Dong, Matthew Johnson-Roberson +1
Teaching robots novel behaviors typically requires motion demonstrations via teleoperation or kinaesthetic teaching, that is, physically guiding the robot. While recent work has ex…
Joint Flow Trajectory Optimization For Feasible Robot Motion Generation from Video Demonstrations
Xiaoxiang Dong, Matthew Johnson-Roberson, Weiming Zhi
Learning from human video demonstrations offers a scalable alternative to teleoperation or kinesthetic teaching, but poses challenges for robot manipulators due to embodiment diffe…
Efficient Construction of Implicit Surface Models From a Single Image for Motion Generation
Wei-Teng Chu, Tianyi Zhang, Matthew Johnson-Roberson +1
Implicit representations have been widely applied in robotics for obstacle avoidance and path planning. In this paper, we explore the problem of constructing an implicit distance r…
Bi-Manual Joint Camera Calibration and Scene Representation
Haozhan Tang, Tianyi Zhang, Matthew Johnson-Roberson +1
Robot manipulation, especially bimanual manipulation, often requires setting up multiple cameras on multiple robot manipulators. Before robot manipulators can generate motion or ev…
From Single Images to Motion Policies via Video-Generation Environment Representations
Weiming Zhi, Ziyong Ma, Tianyi Zhang +1
Autonomous robots typically need to construct representations of their surroundings and adapt their motions to the geometry of their environment. Here, we tackle the problem of con…
GraphSeg: Segmented 3D Representations via Graph Edge Addition and Contraction
Haozhan Tang, Tianyi Zhang, Oliver Kroemer +2
Robots operating in unstructured environments often require accurate and consistent object-level representations. This typically requires segmenting individual objects from the rob…
Infinite Leagues Under the Sea: Photorealistic 3D Underwater Terrain Generation by Latent Fractal Diffusion Models
Tianyi Zhang, Weiming Zhi, Joshua Mangelson +1
This paper tackles the problem of generating representations of underwater 3D terrain. Off-the-shelf generative models, trained on Internet-scale data but not on specialized underw…
Towards Ambiguity-Free Spatial Foundation Model: Rethinking and Decoupling Depth Ambiguity
Xiaohao Xu, Feng Xue, Xiang Li +5
Depth ambiguity is a fundamental challenge in spatial scene understanding, especially in transparent scenes where single-depth estimates fail to capture full 3D structure. Existing…
DOSE3 : Diffusion-based Out-of-distribution detection on SE(3) trajectories
Hongzhe Cheng, Tianyou Zheng, Tianyi Zhang +2
Out-of-Distribution(OOD) detection, a fundamental machine learning task aimed at identifying abnormal samples, traditionally requires model retraining for different inlier distribu…
Scalable Benchmarking and Robust Learning for Noise-Free Ego-Motion and 3D Reconstruction from Noisy Video
Xiaohao Xu, Tianyi Zhang, Shibo Zhao +8
We aim to redefine robust ego-motion estimation and photorealistic 3D reconstruction by addressing a critical limitation: the reliance on noise-free data in existing models. While…