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5,769 results on '"Chen, XiaoPing"'

60. A Q-learning Control Method for a Soft Robotic Arm Utilizing Training Data from a Rough Simulator

Author

Li, Peijin, Wang, Gaotian, Jiang, Hao, Jin, Yusong, Gan, Yinghao, Chen, Xiaoping, and Ji, Jianmin

Subjects
Computer Science - Robotics
Abstract

It is challenging to control a soft robot, where reinforcement learning methods have been applied with promising results. However, due to the poor sample efficiency, reinforcement learning methods require a large collection of training data, which limits their applications. In this paper, we propose a Q-learning controller for a physical soft robot, in which pre-trained models using data from a rough simulator are applied to improve the performance of the controller. We implement the method on our soft robot, i.e., Honeycomb Pneumatic Network (HPN) arm. The experiments show that the usage of pre-trained models can not only reduce the amount of the real-world training data, but also greatly improve its accuracy and convergence rate., Comment: This work has been submitted to the IEEE ROBIO 2021 for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Published
2021

61. Crowd-Aware Robot Navigation for Pedestrians with Multiple Collision Avoidance Strategies via Map-based Deep Reinforcement Learning

Author

Yao1, Shunyi, Chen, Guangda, Qiu, Quecheng, Ma, Jun, Chen, Xiaoping, and Ji, Jianmin

Subjects
Computer Science - Robotics
Abstract

It is challenging for a mobile robot to navigate through human crowds. Existing approaches usually assume that pedestrians follow a predefined collision avoidance strategy, like social force model (SFM) or optimal reciprocal collision avoidance (ORCA). However, their performances commonly need to be further improved for practical applications, where pedestrians follow multiple different collision avoidance strategies. In this paper, we propose a map-based deep reinforcement learning approach for crowd-aware robot navigation with various pedestrians. We use the sensor map to represent the environmental information around the robot, including its shape and observable appearances of obstacles. We also introduce the pedestrian map that specifies the movements of pedestrians around the robot. By applying both maps as inputs of the neural network, we show that a navigation policy can be trained to better interact with pedestrians following different collision avoidance strategies. We evaluate our approach under multiple scenarios both in the simulator and on an actual robot. The results show that our approach allows the robot to successfully interact with various pedestrians and outperforms compared methods in terms of the success rate., Comment: 7 page

Published
2021

77. The effects of Olmesartan/amlodipine administered in the Morning or At Night on nocturnal blood pressure reduction in Chinese patients with mild-moderate essential hypertension (OMAN Trial): study protocol for a prospective, multicenter, randomized, open-label clinical trial {1}

Author

Xu, Mengzhuo, Zhang, Xin, Ye, Runyu, Liu, Xueting, Sun, Lirong, Jia, Shanshan, Zhang, Zhipeng, Li, Xinran, Wang, Ziqiong, Liao, Hang, Shi, Rufeng, Liu, Kai, Wang, Si, Meng, Qingtao, and Chen, Xiaoping

Published
2023
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