2017
DOI: 10.1177/0278364917710318
|View full text |Cite
|
Sign up to set email alerts
|

Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection

Abstract: We describe a learning-based approach to handeye coordination for robotic grasping from monocular images. To learn hand-eye coordination for grasping, we trained a large convolutional neural network to predict the probability that task-space motion of the gripper will result in successful grasps, using only monocular camera images and independently of camera calibration or the current robot pose. This requires the network to observe the spatial relationship between the gripper and objects in the scene, thus le… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

9
1,201
0
2

Year Published

2017
2017
2023
2023

Publication Types

Select...
4
3

Relationship

0
7

Authors

Journals

citations
Cited by 1,595 publications
(1,265 citation statements)
references
References 53 publications
9
1,201
0
2
Order By: Relevance
“…Our approach requires relatively fewer training data for learning, as compared with other CNN based motor learning approaches [7], [8], [18]. Applying RL for learning a motor skill can require a lot of trials [8].…”
Section: Discussionmentioning
confidence: 99%
See 3 more Smart Citations
“…Our approach requires relatively fewer training data for learning, as compared with other CNN based motor learning approaches [7], [8], [18]. Applying RL for learning a motor skill can require a lot of trials [8].…”
Section: Discussionmentioning
confidence: 99%
“…Since we want to use camera images, if a human is always present in the images during demonstrations, a CNN can learn human specific feature. Now during motion reproduction, if the human is not present in the image, then it can result in a failure of the task during reproduction phase [7], [8], [18]. Alternatively a human can provide teleoperated demonstrations as in [11], [18].…”
Section: A Deep-dmpmentioning
confidence: 99%
See 2 more Smart Citations
“…While the results are impressive, these methods usually require extensive amount of experimental data [17,25] or relatively restrictive settings [16]. It is unclear whether these method would work directly on more dynamic motor skills in the real-world, such as locomotion.…”
Section: Related Work a Deep Reinforcement Learningmentioning
confidence: 99%