4 citations · 5 across the 5 of their papers we have counts for
5 papers · 1 filter
Tactile-Guided Robot Learning: From Representation Learning to Foundation Models
Kiyanoush Nazari, Luchen Li, Haoshu Fang +4
Tactile sensing is becoming central to robust, adaptive, and dexterous robotic manipulation. Beyond its traditional role in contact detection and reactive control, touch is increas…
DemoBot: Efficient Learning of Bimanual Manipulation with Dexterous Hands From Third-Person Human Videos
Yucheng Xu, Xiaofeng Mao, Elle Miller +4
This work presents DemoBot, a learning framework that enables a dual-arm, multi-finger robotic system to acquire complex manipulation skills from a single unannotated RGB-D video d…
DexSkills: Skill Segmentation Using Haptic Data for Learning Autonomous Long-Horizon Robotic Manipulation Tasks
Xiaofeng Mao, Gabriele Giudici, Claudio Coppola +4
Effective execution of long-horizon tasks with dexterous robotic hands remains a significant challenge in real-world problems. While learning from human demonstrations have shown e…
Language-guided Robot Grasping: CLIP-based Referring Grasp Synthesis in Clutter
Georgios Tziafas, Yucheng Xu, Arushi Goel +3
Robots operating in human-centric environments require the integration of visual grounding and grasping capabilities to effectively manipulate objects based on user instructions. T…
Learning Fine Pinch-Grasp Skills using Tactile Sensing from A Few Real-world Demonstrations
Xiaofeng Mao, Yucheng Xu, Ruoshi Wen +5
Imitation learning for robot dexterous manipulation, especially with a real robot setup, typically requires a large number of demonstrations. In this paper, we present a data-effic…