8 papers · 1 filter
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…
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…