Learning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach

by   Alessia Bertugli, et al.

In this paper, we propose a deep reinforcement learning (DRL) solution to the grasping problem using 2.5D images as the only source of information. In particular, we developed a simulated environment where a robot equipped with a vacuum gripper has the aim of reaching blocks with planar surfaces. These blocks can have different dimensions, shapes, position and orientation. Unity 3D allowed us to simulate a real-world setup, where a depth camera is placed in a fixed position and the stream of images is used by our policy network to learn how to solve the task. We explored different DRL algorithms and problem configurations. The experiments demonstrated the effectiveness of the proposed DRL algorithm applied to grasp tasks guided by visual depth camera inputs. When using the proper policy, the proposed method estimates a robot tool configuration that reaches the object surface with negligible position and orientation errors. This is, to the best of our knowledge, the first successful attempt of using 2.5D images only as of the input of a DRL algorithm, to solve the grasping problem regressing 3D world coordinates.


page 2

page 3

page 6


Robotic Grasp Manipulation Using Evolutionary Computing and Deep Reinforcement Learning

Intelligent Object manipulation for grasping is a challenging problem fo...

Robotic Grasping using Deep Reinforcement Learning

In this work, we present a deep reinforcement learning based method to s...

A Grasp Pose is All You Need: Learning Multi-fingered Grasping with Deep Reinforcement Learning from Vision and Touch

Multi-fingered robotic hands could enable robots to perform sophisticate...

Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee

This paper presents a deep reinforcement learning (DRL) algorithm for or...

DRL-ISP: Multi-Objective Camera ISP with Deep Reinforcement Learning

In this paper, we propose a multi-objective camera ISP framework that ut...

Binarized P-Network: Deep Reinforcement Learning of Robot Control from Raw Images on FPGA

This paper explores a Deep Reinforcement Learning (DRL) approach for des...

InsertionNet – A Scalable Solution for Insertion

Complicated assembly processes can be described as a sequence of two mai...

Please sign up or login with your details

Forgot password? Click here to reset