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pro vyhledávání: '"Qiu, Quecheng"'
Autor:
Sheng, Yu, Lin, Runfeng, Wang, Lidian, Qiu, Quecheng, Zhang, YanYong, Zhang, Yu, Hua, Bei, Ji, Jianmin
Combining accurate geometry with rich semantics has been proven to be highly effective for language-guided robotic manipulation. Existing methods for dynamic scenes either fail to update in real-time or rely on additional depth sensors for simple sce
Externí odkaz:
http://arxiv.org/abs/2410.15730
PathRL: An End-to-End Path Generation Method for Collision Avoidance via Deep Reinforcement Learning
Robot navigation using deep reinforcement learning (DRL) has shown great potential in improving the performance of mobile robots. Nevertheless, most existing DRL-based navigation methods primarily focus on training a policy that directly commands the
Externí odkaz:
http://arxiv.org/abs/2310.13295
Autor:
Chen, Yuan, Qiu, Quecheng, Liu, Xiangyu, Chen, Guangda, Yao, Shunyi, Peng, Jie, Ji, Jianmin, Zhang, Yanyong
Reliable localization is crucial for autonomous robots to navigate efficiently and safely. Some navigation methods can plan paths with high localizability (which describes the capability of acquiring reliable localization). By following these paths,
Externí odkaz:
http://arxiv.org/abs/2303.12354
Existing navigation policies for autonomous robots tend to focus on collision avoidance while ignoring human-robot interactions in social life. For instance, robots can pass along the corridor safer and easier if pedestrians notice them. Sounds have
Externí odkaz:
http://arxiv.org/abs/2203.16154
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
Externí odkaz:
http://arxiv.org/abs/2109.02541
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