16 citations · 32 across the 12 of their papers we have counts for
13 papers
Hydra-0: Action Flow for Generalist World Modeling and Control
Hongyu Li, Bowen Wen, Xinghao Zhu +8
We introduce Hydra-0, a generalist world model conditioned on action flow, which represents robot actions as pixel motion. This shared visual interface enables generalist world mod…
Deform360: A Massive Multi-view Visuotactile Dataset for Deformable World Models
Hongyu Li, Wanjia Fu, Xiaoyan Cong +11
Predicting object dynamics (i.e., world modeling) is a fundamental challenge for robotic manipulation, and modeling deformable objects presents a particularly difficult case due to…
NovaPlan: Zero-Shot Long-Horizon Manipulation via Closed-Loop Video Language Planning
Jiahui Fu, Junyu Nan, Lingfeng Sun +7
Solving complex long-horizon robotic tasks requires joint reasoning over abstract task structure and low-level physical interaction. While combining Vision-Language Models (VLMs) a…
NovaFlow: Zero-Shot Manipulation via Actionable Flow from Generated Videos
Hongyu Li, Lingfeng Sun, Yafei Hu +4
Enabling robots to execute novel manipulation tasks zero-shot is a central goal in robotics. Most existing methods assume in-distribution tasks or rely on fine-tuning with embodime…
ViTa-Zero: Zero-shot Visuotactile Object 6D Pose Estimation
Hongyu Li, James Akl, Srinath Sridhar +2
Object 6D pose estimation is a critical challenge in robotics, particularly for manipulation tasks. While prior research combining visual and tactile (visuotactile) information has…
V-HOP: Visuo-Haptic 6D Object Pose Tracking
Hongyu Li, Mingxi Jia, Tuluhan Akbulut +3
Humans naturally integrate vision and haptics for robust object perception during manipulation. The loss of either modality significantly degrades performance. Inspired by this mul…
HyperTaxel: Hyper-Resolution for Taxel-Based Tactile Signals Through Contrastive Learning
Hongyu Li, Snehal Dikhale, Jinda Cui +2
To achieve dexterity comparable to that of humans, robots must intelligently process tactile sensor data. Taxel-based tactile signals often have low spatial-resolution, with non-st…
StereoNavNet: Learning to Navigate using Stereo Cameras with Auxiliary Occupancy Voxels
Hongyu Li, Taskin Padir, Huaizu Jiang
Visual navigation has received significant attention recently. Most of the prior works focus on predicting navigation actions based on semantic features extracted from visual encod…
ODTFormer: Efficient Obstacle Detection and Tracking with Stereo Cameras Based on Transformer
Tianye Ding, Hongyu Li, Huaizu Jiang
Obstacle detection and tracking represent a critical component in robot autonomous navigation. In this paper, we propose ODTFormer, a Transformer-based model to address both obstac…
ViHOPE: Visuotactile In-Hand Object 6D Pose Estimation with Shape Completion
Hongyu Li, Snehal Dikhale, Soshi Iba +1
In this letter, we introduce ViHOPE, a novel framework for estimating the 6D pose of an in-hand object using visuotactile perception. Our key insight is that the accuracy of the 6D…
E(2)-Equivariant Graph Planning for Navigation
Linfeng Zhao, Hongyu Li, Taskin Padir +2
Learning for robot navigation presents a critical and challenging task. The scarcity and costliness of real-world datasets necessitate efficient learning approaches. In this letter…
StereoVoxelNet: Real-Time Obstacle Detection Based on Occupancy Voxels from a Stereo Camera Using Deep Neural Networks
Hongyu Li, Zhengang Li, Neset Unver Akmandor +3
Obstacle detection is a safety-critical problem in robot navigation, where stereo matching is a popular vision-based approach. While deep neural networks have shown impressive resu…
Deep Reinforcement Learning based Robot Navigation in Dynamic Environments using Occupancy Values of Motion Primitives
Neşet Ünver Akmandor, Hongyu Li, Gary Lvov +2
This paper presents a Deep Reinforcement Learning based navigation approach in which we define the occupancy observations as heuristic evaluations of motion primitives, rather than…