2 citations · 5 across the 8 of their papers we have counts for
12 papers
ArtManip: Category-Level Articulated In-Hand Manipulation
Yang Yang, Tengyu Liu, Puhao Li +6
Category-level in-hand manipulation of articulated objects is a formidable yet underexplored challenge for dexterous robotic hands. This difficulty stems from two core bottlenecks:…
EaDex: A Cross-Embodiment Dexterous Manipulation Framework from Low-Cost Demonstrations
Qian Zhao, Xin Tong, Chengdong Wu +2
Dexterous manipulation learning has long been hindered by the high costs of data and training, as pure reinforcement learning typically requires large-scale interactive exploration…
Attribute-Based Robotic Grasping with Data-Efficient Adaptation
Yang Yang, Houjian Yu, Xibai Lou +2
Robotic grasping is one of the most fundamental robotic manipulation tasks and has been the subject of extensive research. However, swiftly teaching a robot to grasp a novel target…
A Parameter-Efficient Tuning Framework for Language-guided Object Grounding and Robot Grasping
Houjian Yu, Mingen Li, Alireza Rezazadeh +2
The language-guided robot grasping task requires a robot agent to integrate multimodal information from both visual and linguistic inputs to predict actions for target-driven grasp…
Adversarial Object Rearrangement in Constrained Environments with Heterogeneous Graph Neural Networks
Xibai Lou, Houjian Yu, Ross Worobel +2
Adversarial object rearrangement in the real world (e.g., previously unseen or oversized items in kitchens and stores) could benefit from understanding task scenes, which inherentl…
IOSG: Image-driven Object Searching and Grasping
Houjian Yu, Xibai Lou, Yang Yang +1
When robots retrieve specific objects from cluttered scenes, such as home and warehouse environments, the target objects are often partially occluded or completely hidden. Robots a…