Search Results for author:
Found 8 papers, 5 papers with code
DexDiffuser: Generating Dexterous Grasps with Diffusion Models
We introduce DexDiffuser, a novel dexterous grasping method that generates, evaluates, and refines grasps on partial object point clouds.
Active Visuo-Haptic Object Shape Completion
We also propose a new simulation environment for this purpose.
DDGC: Generative Deep Dexterous Grasping in Clutter
Multi-finger grasping in cluttered scenes, on the other hand, remains mostly unexplored due to the added difficulty of reasoning over obstacles which greatly increases the computational time to generate high-quality collision-free grasps.
Multi-FinGAN: Generative Coarse-To-Fine Sampling of Multi-Finger Grasps
While there exists many methods for manipulating rigid objects with parallel-jaw grippers, grasping with multi-finger robotic hands remains a quite unexplored research topic.
Beyond Top-Grasps Through Scene Completion
Current end-to-end grasp planning methods propose grasps in the order of seconds that attain high grasp success rates on a diverse set of objects, but often by constraining the workspace to top-grasps.
Robust Grasp Planning Over Uncertain Shape Completions
We present a method for planning robust grasps over uncertain shape completed objects.
Robotics
Deep Network Uncertainty Maps for Indoor Navigation
Most mobile robots for indoor use rely on 2D laser scanners for localization, mapping and navigation.
Hallucinating robots: Inferring Obstacle Distances from Partial Laser Measurements
However, those sensors are unable to correctly provide distance to obstacles such as glass panels and tables whose actual occupancy is invisible at the height the sensor is measuring.
