DDGC: Generative Deep Dexterous Grasping in Clutter

03/08/2021
by   Jens Lundell, et al.
0

Recent advances in multi-fingered robotic grasping have enabled fast 6-Degrees-Of-Freedom (DOF) single object grasping. 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. In this work we address such limitations by introducing DDGC, a fast generative multi-finger grasp sampling method that can generate high quality grasps in cluttered scenes from a single RGB-D image. DDGC is built as a network that encodes scene information to produce coarse-to-fine collision-free grasp poses and configurations. We experimentally benchmark DDGC against the simulated-annealing planner in GraspIt! on 1200 simulated cluttered scenes and 7 real world scenes. The results show that DDGC outperforms the baseline on synthesizing high-quality grasps and removing clutter while being 5 times faster. This, in turn, opens up the door for using multi-finger grasps in practical applications which has so far been limited due to the excessive computation time needed by other methods.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 5

page 6

page 7

12/17/2020

Multi-FinGAN: Generative Coarse-To-Fine Sampling of Multi-Finger Grasps

While there exists a large number of methods for manipulating rigid obje...
10/06/2017

Planning High-Quality Grasps using Mean Curvature Object Skeletons

In this work, we present a grasp planner which integrates two sources of...
03/08/2019

Pixel-Attentive Policy Gradient for Multi-Fingered Grasping in Cluttered Scenes

Recent advances in on-policy reinforcement learning (RL) methods enabled...
10/31/2019

S4G: Amodal Single-view Single-Shot SE(3) Grasp Detection in Cluttered Scenes

Grasping is among the most fundamental and long-lasting problems in robo...
04/01/2021

Collision-Aware Target-Driven Object Grasping in Constrained Environments

Grasping a novel target object in constrained environments (e.g., walls,...
08/10/2020

Grasping Field: Learning Implicit Representations for Human Grasps

In recent years, substantial progress has been made on robotic grasping ...
01/25/2018

Collaborative Large-Scale Dense 3D Reconstruction with Online Inter-Agent Pose Optimisation

Reconstructing dense, volumetric models of real-world 3D scenes is impor...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.