2 citations · 2 across the 7 of their papers we have counts for
6 papers · 1 filter
Grasping a Handful: Sequential Multi-Object Dexterous Grasp Generation
Haofei Lu, Yifei Dong, Zehang Weng +3
We introduce the sequential multi-object robotic grasp sampling algorithm SeqGrasp that can robustly synthesize stable grasps on diverse objects using the robotic hand's partial De…
Interactive Perception for Deformable Object Manipulation
Zehang Weng, Peng Zhou, Hang Yin +4
Interactive perception enables robots to manipulate the environment and objects to bring them into states that benefit the perception process. Deformable objects pose challenges to…
DexDiffuser: Generating Dexterous Grasps with Diffusion Models
Zehang Weng, Haofei Lu, Danica Kragic +1
We introduce DexDiffuser, a novel dexterous grasping method that generates, evaluates, and refines grasps on partial object point clouds. DexDiffuser includes the conditional diffu…
CAPGrasp: An Continuous Approach-Constrained Generative Grasp Sampler
Zehang Weng, Haofei Lu, Jens Lundell +1
We propose CAPGrasp, an 6-DoF continuous approach-constrained generative grasp sampler. It includes a novel learning strategy for trai…
GoNet: An Approach-Constrained Generative Grasp Sampling Network
Zehang Weng, Haofei Lu, Jens Lundell +1
This work addresses the problem of learning approach-constrained data-driven grasp samplers. To this end, we propose GoNet: a generative grasp sampler that can constrain the grasp…
Graph-based Task-specific Prediction Models for Interactions between Deformable and Rigid Objects
Zehang Weng, Fabian Paus, Anastasiia Varava +3
Capturing scene dynamics and predicting the future scene state is challenging but essential for robotic manipulation tasks, especially when the scene contains both rigid and deform…