4 citations · 7 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
Learning Long-Horizon Robot Manipulation Skills via Privileged Action
Xiaofeng Mao, Yucheng Xu, Zhaole Sun +3
Long-horizon contact-rich tasks are challenging to learn with reinforcement learning, due to ineffective exploration of high-dimensional state spaces with sparse rewards. The learn…
DHP-Mapping: A Dense Panoptic Mapping System with Hierarchical World Representation and Label Optimization Techniques
Tianshuai Hu, Jianhao Jiao, Yucheng Xu +3
Maps provide robots with crucial environmental knowledge, thereby enabling them to perform interactive tasks effectively. Easily accessing accurate abstract-to-detailed geometric a…
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…
Instance-wise Grasp Synthesis for Robotic Grasping
Yucheng Xu, Mohammadreza Kasaei, Hamidreza Kasaei +1
Generating high-quality instance-wise grasp configurations provides critical information of how to grasp specific objects in a multi-object environment and is of high importance fo…