2020/12/02 by Arturo Cruz-Maya, Cruz-Maya, Arturo · 1 citation
Computer Science · Psychology · #Artificial Intelligence (cs.AI) #Artificial intelligence #Computer science #Computer vision #FOS: Computer and information sciences #Humanoid robot #Human–computer interaction #Mobile robot #Mobile robot navigation #Path (computing) #Reinforcement Learning in Robotics #Robot #Robot control #Robot learning #Robotic Path Planning Algorithms #Robotics (cs.RO) #Simulation #Social Robot Interaction and HRI #Social robot #cs.AI #cs.RO
paper · pdf · doi:10.48550/arxiv.2012.01206
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2020/12/02 · arxiv created 2021/01/27 · arxiv updated 2021/01/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Robot navigation in human semi-static and crowded environments can lead to the freezing problem, where the robot can not move due to the presence of humans standing on its path and no other path is available. Classical approaches of robot navigation do not provide a solution for this problem. In such situations, the robot could interact with the humans in order to clear its path instead of considering them as unanimated obstacles. In this work, we propose a robot behavior for a wheeled humanoid robot that complains with social norms for clearing its path when the robot is frozen due to the presence of humans. The behavior consists of two modules: 1) A detection module, which make use of the Yolo v3 algorithm trained to detect human hands and human arms. 2) A gesture module, which make use of a policy trained in simulation using the Proximal Policy Optimization algorithm. Orchestration of the two models is done using the ROS framework.