DeepAI AI Chat
Log In Sign Up

Collision-Aware Target-Driven Object Grasping in Constrained Environments

04/01/2021
by   Xibai Lou, et al.
4

Grasping a novel target object in constrained environments (e.g., walls, bins, and shelves) requires intensive reasoning about grasp pose reachability to avoid collisions with the surrounding structures. Typical 6-DoF robotic grasping systems rely on the prior knowledge about the environment and intensive planning computation, which is ungeneralizable and inefficient. In contrast, we propose a novel Collision-Aware Reachability Predictor (CARP) for 6-DoF grasping systems. The CARP learns to estimate the collision-free probabilities for grasp poses and significantly improves grasping in challenging environments. The deep neural networks in our approach are trained fully by self-supervision in simulation. The experiments in both simulation and the real world show that our approach achieves more than 75 novel objects in various surrounding structures. The ablation study demonstrates the effectiveness of the CARP, which improves the 6-DoF grasping rate by 95.7

READ FULL TEXT

page 1

page 3

page 5

page 6

10/14/2019

Learning to Generate 6-DoF Grasp Poses with Reachability Awareness

Motivated by the stringent requirements of unstructured real-world where...
06/29/2022

Neural Motion Fields: Encoding Grasp Trajectories as Implicit Value Functions

The pipeline of current robotic pick-and-place methods typically consist...
02/04/2020

Deep Differentiable Grasp Planner for High-DOF Grippers

We present an end-to-end algorithm for training deep neural networks to ...
06/07/2022

Learning Mobile Manipulation

Providing mobile robots with the ability to manipulate objects has, desp...
03/18/2021

Dynamic Grasping with Reachability and Motion Awareness

Grasping in dynamic environments presents a unique set of challenges. A ...
03/08/2021

DDGC: Generative Deep Dexterous Grasping in Clutter

Recent advances in multi-fingered robotic grasping have enabled fast 6-D...
11/28/2020

AdaGrasp: Learning an Adaptive Gripper-Aware Grasping Policy

This paper aims to improve robots' versatility and adaptability by allow...