10 citations · 15 across the 12 of their papers we have counts for
13 papers
Efficient Image Synthesis with Sphere Latent Encoder
Tung Do, Thuan Hoang Nguyen, Hao Li
Few-step image generation has seen rapid progress, with consistency and meanflow-based methods significantly reducing the number of sampling steps. Despite their low inference cost…
FFAvatar: Few-Shot, Feed-Forward, and Generalizable Avatar Reconstruction
Thuan Hoang Nguyen, Jiahao Luo, Yinyu Nie +3
Avatar reconstruction has traditionally relied on per-subject optimization that requires hours of computation or on expensive preprocessing that limits scalability. We introduce FF…
IMPASTO: Integrating Model-Based Planning with Learned Dynamics Models for Robotic Oil Painting Reproduction
Yingke Wang, Hao Li, Yifeng Zhu +6
Robotic reproduction of oil paintings using soft brushes and pigments requires force-sensitive control of deformable tools, prediction of brushstroke effects, and multi-step stroke…
UMI-Underwater: Learning Underwater Manipulation without Underwater Teleoperation
Hao Li, Long Yin Chung, Jack Goler +5
Underwater robotic grasping is difficult due to degraded, highly variable imagery and the expense of collecting diverse underwater demonstrations. We introduce a system that (i) au…
Multimodal Sensing for Robot-Assisted Sub-Tissue Feature Detection in Physiotherapy Palpation
Tian-Ao Ren, Jorge Garcia, Seongheon Hong +6
Robotic palpation relies on force sensing, but force signals in soft-tissue environments are variable and cannot reliably reveal subtle subsurface features. We present a compact mu…
TypeTele: Releasing Dexterity in Teleoperation by Dexterous Manipulation Types
Yuhao Lin, Yi-Lin Wei, Haoran Liao +6
Dexterous teleoperation plays a crucial role in robotic manipulation for real-world data collection and remote robot control. Previous dexterous teleoperation mostly relies on hand…
TacCap: A Wearable FBG-Based Tactile Sensor for Seamless Human-to-Robot Skill Transfer
Chengyi Xing, Hao Li, Yi-Lin Wei +6
Tactile sensing is essential for dexterous manipulation, yet large-scale human demonstration datasets lack tactile feedback, limiting their effectiveness in skill transfer to robot…
Whisker-Inspired Tactile Sensing: A Sim2Real Approach for Precise Underwater Contact Tracking
Hao Li, Chengyi Xing, Saad Khan +2
Aquatic mammals, such as pinnipeds, utilize their whiskers to detect and discriminate objects and analyze water movements, inspiring the development of robotic whiskers for sensing…
Navigation and 3D Surface Reconstruction from Passive Whisker Sensing
Michael A. Lin, Hao Li, Chengyi Xing +1
Whiskers provide a way to sense surfaces in the immediate environment without disturbing it. In this paper we present a method for using highly flexible, curved, passive whiskers m…
Grasp as You Say: Language-guided Dexterous Grasp Generation
Yi-Lin Wei, Jian-Jian Jiang, Chengyi Xing +5
This paper explores a novel task "Dexterous Grasp as You Say" (DexGYS), enabling robots to perform dexterous grasping based on human commands expressed in natural language. However…
The ObjectFolder Benchmark: Multisensory Learning with Neural and Real Objects
Ruohan Gao, Yiming Dou, Hao Li +5
We introduce the ObjectFolder Benchmark, a benchmark suite of 10 tasks for multisensory object-centric learning, centered around object recognition, reconstruction, and manipulatio…
Sonicverse: A Multisensory Simulation Platform for Embodied Household Agents that See and Hear
Ruohan Gao, Hao Li, Gokul Dharan +6
Developing embodied agents in simulation has been a key research topic in recent years. Exciting new tasks, algorithms, and benchmarks have been developed in various simulators. Ho…
See, Hear, and Feel: Smart Sensory Fusion for Robotic Manipulation
Hao Li, Yizhi Zhang, Junzhe Zhu +7
Humans use all of their senses to accomplish different tasks in everyday activities. In contrast, existing work on robotic manipulation mostly relies on one, or occasionally two mo…