arXiv Computer Vision
Vision-Based 6-DoF Grasp Pose Estimation for Robot Cloth Unfolding
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv AI
Jun 17
DeMaVLA: A Vision-Language-Action Foundation Model for Generalizable Deformable Manipulation
arXiv:2605. 31286v2 Announce Type: replace-cross Abstract: Real-world household robots require Vision-Language-Action (VLA) foundation models that can acquire reusable manipulation skills across diverse objects, task conditions, and household environments.
arXiv AI
Aug 21
GOAG: Generative and Object-Agnostic Grasp Planner for Dexterous Robotic Manipulation
arXiv:2608. 19759v1 Announce Type: cross Abstract: Multifingered grasping is a crucial robotic skill, but current deep-learning grasp planners often struggle to generalize to new objects because they are trained on limited, object-specific datasets.
arXiv AI
Sep 10
HeteroGenManip: Generalizable Manipulation For Heterogeneous Object Interactions
arXiv:2605.10201v3 Announce Type: replace-cross Abstract: Generalizable manipulation involving cross-type object interactions is a critical yet challenging capability in robotics. To reliably accompl...
arXiv Machine Learning
Jul 16
Topology-Agnostic Mesh Reconstruction of Deformable Objects from Sparse Touch
arXiv:2607. 13479v1 Announce Type: cross Abstract: Estimating the full shape of a deformable object is especially challenging when vision is unavailable: in the dark, inside an opaque bag, behind the manipulating hand, or under heavy self-occlusion.
arXiv Computer Vision
Sep 3
MultiGraspNet: A Multitask 3D Vision Model for Multi-gripper Robotic Grasping
MultiGraspNet is a multitask 3D vision model that simultaneously predicts feasible poses for both parallel and vacuum grippers, allowing a single robot to handle multiple end effectors. Trained on the aligned GraspNet-1Billion and SuctionNet-1Billion datasets, it generates graspability masks that quantify the suitability of each scene point for successful grasps. With only 15.75 M parameters, the model achieves fast inference on a single GPU and demonstrates competitive performance against single-task models while reducing computational cost, as shown in extensive experiments and real‑world tests on a single‑arm multi‑gripper setup.
arXiv AI
Jun 9
EgoAERO: Learning Dexterous Manipulation from a Single Egocentric Video without Object Assets
arXiv:2606. 08057v1 Announce Type: cross Abstract: Egocentric RGB-D videos offer a natural source of human dexterous manipulation demonstrations, but existing data is difficult to use for robot learning because object pose, geometry, and contact information are often missing or require pre-scanned object assets.