Deep learning approaches to grasp synthesis: A review
Grasping is the process of picking up an object by applying forces and torques at a set of
contacts. Recent advances in deep learning methods have allowed rapid progress in robotic …
contacts. Recent advances in deep learning methods have allowed rapid progress in robotic …
Learning for a robot: Deep reinforcement learning, imitation learning, transfer learning
J Hua, L Zeng, G Li, Z Ju - Sensors, 2021 - mdpi.com
Dexterous manipulation of the robot is an important part of realizing intelligence, but
manipulators can only perform simple tasks such as sorting and packing in a structured …
manipulators can only perform simple tasks such as sorting and packing in a structured …
Graspnet-1billion: A large-scale benchmark for general object grasping
Object grasping is critical for many applications, which is also a challenging computer vision
problem. However, for cluttered scene, current researches suffer from the problems of …
problem. However, for cluttered scene, current researches suffer from the problems of …
Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics
To reduce data collection time for deep learning of robust robotic grasp plans, we explore
training from a synthetic dataset of 6.7 million point clouds, grasps, and analytic grasp …
training from a synthetic dataset of 6.7 million point clouds, grasps, and analytic grasp …
Closing the loop for robotic grasping: A real-time, generative grasp synthesis approach
This paper presents a real-time, object-independent grasp synthesis method which can be
used for closed-loop grasping. Our proposed Generative Grasping Convolutional Neural …
used for closed-loop grasping. Our proposed Generative Grasping Convolutional Neural …
State-of-the-art deep learning: Evolving machine intelligence toward tomorrow's intelligent network traffic control systems
Currently, the network traffic control systems are mainly composed of the Internet core and
wired/wireless heterogeneous backbone networks. Recently, these packet-switched …
wired/wireless heterogeneous backbone networks. Recently, these packet-switched …
Affordpose: A large-scale dataset of hand-object interactions with affordance-driven hand pose
How human interact with objects depends on the functional roles of the target objects, which
introduces the problem of affordance-aware hand-object interaction. It requires a large …
introduces the problem of affordance-aware hand-object interaction. It requires a large …
Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
We describe a learning-based approach to hand-eye coordination for robotic grasping from
monocular images. To learn hand-eye coordination for grasping, we trained a large …
monocular images. To learn hand-eye coordination for grasping, we trained a large …
Pointnetgpd: Detecting grasp configurations from point sets
In this paper, we propose an end-to-end grasp evaluation model to address the challenging
problem of localizing robot grasp configurations directly from the point cloud. Compared to …
problem of localizing robot grasp configurations directly from the point cloud. Compared to …
Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges
In the last few years, there has been an exponential increase in the usage of the
autonomous vehicles across the globe. It is due to an exponential increase in the popularity …
autonomous vehicles across the globe. It is due to an exponential increase in the popularity …