5 papers
GraspVLA: a Grasping Foundation Model Pre-trained on Billion-scale Synthetic Action Data
Shengliang Deng, Mi Yan, Songlin Wei +10
Embodied foundation models are gaining increasing attention for their zero-shot generalization, scalability, and adaptability to new tasks through few-shot post-training. However,…
Dexonomy: Synthesizing All Dexterous Grasp Types in a Grasp Taxonomy
Jiayi Chen, Yubin Ke, Lin Peng +1
Generalizable dexterous grasping with suitable grasp types is a fundamental skill for intelligent robots. Developing such skills requires a large-scale and high-quality dataset tha…
BODex: Scalable and Efficient Robotic Dexterous Grasp Synthesis Using Bilevel Optimization
Jiayi Chen, Yubin Ke, He Wang
Robotic dexterous grasping is important for interacting with the environment. To unleash the potential of data-driven models for dexterous grasping, a large-scale, high-quality dat…
RoboHanger: Learning Generalizable Robotic Hanger Insertion for Diverse Garments
Yuxing Chen, Songlin Wei, Bowen Xiao +4
For the task of hanging clothes, learning how to insert a hanger into a garment is a crucial step, but has rarely been explored in robotics. In this work, we address the problem of…
D3RoMa: Disparity Diffusion-based Depth Sensing for Material-Agnostic Robotic Manipulation
Songlin Wei, Haoran Geng, Jiayi Chen +6
Depth sensing is an important problem for 3D vision-based robotics. Yet, a real-world active stereo or ToF depth camera often produces noisy and incomplete depth which bottlenecks…