2 citations · 6 across the 8 of their papers we have counts for
9 papers
A self-supervised learning-based 6-DOF grasp planning method for manipulator
Gang Peng, Zhenyu Ren, Hao Wang +1
To realize a robust robotic grasping system for unknown objects in an unstructured environment, large amounts of grasp data and 3D model data for the object are required, the sizes…
A Pushing-Grasping Collaborative Method Based on Deep Q-Network Algorithm in Dual Perspectives
Peng Gang, Liao Jinhu, Guan Shangbin
Aiming at the traditional grasping method for manipulators based on 2D camera, when faced with the scene of gathering or covering, it can hardly perform well in unstructured scenes…
Deep Reinforcement Learning with a Stage Incentive Mechanism of Dense Reward for Robotic Trajectory Planning
Gang Peng, Jin Yang, Xinde Lia +1
(This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.) To improve t…
A Visual Kinematics Calibration Method for Manipulator Based on Nonlinear Optimization
Peng Gang, Wang Zhihao, Yang Jin +1
The traditional kinematic calibration method for manipulators requires precise three-dimensional measuring instruments to measure the end pose, which is not only expensive due to t…
Calibration of the internal and external parameters of wheeled robot mobile chasses and inertial measurement units based on nonlinear optimization
Gang Peng, Zezao Lu, Zejie Tan +2
Mobile robot positioning, mapping, and navigation systems generally employ an inertial measurement unit (IMU) to obtain the acceleration and angular velocity of the robot. However,…
Single upper limb pose estimation method based on improved stacked hourglass network
Gang Peng, Yuezhi Zheng, Jianfeng Li +2
At present, most high-accuracy single-person pose estimation methods have high computational complexity and insufficient real-time performance due to the complex structure of the n…
Attention to Refine through Multi-Scales for Semantic Segmentation
Shiqi Yang, Gang Peng
This paper proposes a novel attention model for semantic segmentation, which aggregates multi-scale and context features to refine prediction. Specifically, the skeleton convolutio…
Parallel Convolutional Networks for Image Recognition via a Discriminator
Shiqi Yang, Gang Peng
In this paper, we introduce a simple but quite effective recognition framework dubbed D-PCN, aiming at enhancing feature extracting ability of CNN. The framework consists of two pa…
D-PCN: Parallel Convolutional Networks for Image Recognition via a Discriminator
Shiqi Yang, Gang Peng
In this paper, we introduce a simple but quite effective recognition framework dubbed D-PCN, aiming at enhancing feature extracting ability of CNN. The framework consists of two pa…