To provide a solution to this problem, we propose a novel branched network G-CIL for the navigation policy learning.
We suggest that introducing an attention mechanism to infer the importance of different neighbors is critical for accurate trajectory prediction in scenes with varying crowd size.
Fully investigating the social interactions within the crowd is crucial for accurate pedestrian trajectory prediction.
Forecasting human trajectories is critical for tasks such as robot crowd navigation and autonomous driving.
no code implementations • 16 Apr 2020 • Tianyu Liu, Qinghai Liao, Lu Gan, Fulong Ma, Jie Cheng, Xupeng Xie, Zhe Wang, Yingbing Chen, Yilong Zhu, Shuyang Zhang, Zhengyong Chen, Yang Liu, Meng Xie, Yang Yu, Zitong Guo, Guang Li, Peidong Yuan, Dong Han, Yuying Chen, Haoyang Ye, Jianhao Jiao, Peng Yun, Zhenhua Xu, Hengli Wang, Huaiyang Huang, Sukai Wang, Peide Cai, Yuxiang Sun, Yandong Liu, Lujia Wang, Ming Liu
Moreover, many countries have imposed tough lockdown measures to reduce the virus transmission (e. g., retail, catering) during the pandemic, which causes inconveniences for human daily life.
Previous work has shown the power of deep reinforcement learning frameworks to train efficient policies.
Cognitive diagnosis is a fundamental issue in intelligent education, which aims to discover the proficiency level of students on specific knowledge concepts.
Vision-based autonomous driving through imitation learning mimics the behaviors of human drivers by training on pairs of data of raw driver-view images and actions.
The basic idea behind behavioral cloning is to have the neural network learn from observing a human expert's behavior.
End-to-end visual-based imitation learning has been widely applied in autonomous driving.