9 papers
Event Cameras versus Frame Cameras for Synthetic-Aperture Imaging: A Simulation Study
Jakob Hinterholzer, Mohamed Youssef, Oliver Bimber
Synthetic-aperture imaging (SAI) removes occluders such as foliage by integrating many views of a scene along a camera path. It has so far been used mainly with frame cameras. We a…
MoDex: A Diffusion Policy for Sequential Multi-Object Dexterous Grasping
Haofei Lu, Hongjia Liu, Yifei Dong +3
This work addresses sequentially grasping multiple objects with a single dexterous hand without releasing those already held. Most dexterous grasping methods commit all of the hand…
SCAR: Self-Supervised Continuous Action Representation Learning
Hongjia Liu, Fan Feng, Minghao Fu +3
Despite the central role of action in embodied intelligence, learning transferable action representations from visual transitions remains a fundamental challenge, particularly when…
Grasp as You Dream: Imitating Functional Grasping from Generated Human Demonstrations
Chao Tang, Jiacheng Xu, Haofei Lu +4
Building generalist robots capable of performing functional grasping in everyday, open-world environments remains a significant challenge due to the vast diversity of objects and t…
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…
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…
Enabling Robot Manipulation of Soft and Rigid Objects with Vision-based Tactile Sensors
Michael C. Welle, Martina Lippi, Haofei Lu +3
Endowing robots with tactile capabilities opens up new possibilities for their interaction with the environment, including the ability to handle fragile and/or soft objects. In thi…
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…