1 citations · 1 across the 5 of their papers we have counts for
5 papers
MSGField: A Unified Scene Representation Integrating Motion, Semantics, and Geometry for Robotic Manipulation
Yu Sheng, Runfeng Lin, Lidian Wang +5
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…
PathRL: An End-to-End Path Generation Method for Collision Avoidance via Deep Reinforcement Learning
Wenhao Yu, Jie Peng, Quecheng Qiu +3
Robot navigation using deep reinforcement learning (DRL) has shown great potential in improving the performance of mobile robots. Nevertheless, most existing DRL-based navigation m…
Deep Reinforcement Learning for Localizability-Enhanced Navigation in Dynamic Human Environments
Yuan Chen, Quecheng Qiu, Xiangyu Liu +5
Reliable localization is crucial for autonomous robots to navigate efficiently and safely. Some navigation methods can plan paths with high localizability (which describes the capa…
Learning to Socially Navigate in Pedestrian-rich Environments with Interaction Capacity
Quecheng Qiu, Shunyi Yao, Jing Wang +3
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…
Crowd-Aware Robot Navigation for Pedestrians with Multiple Collision Avoidance Strategies via Map-based Deep Reinforcement Learning
Shunyi Yao1, Guangda Chen, Quecheng Qiu +3
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 so…