Finding a path free from obstacles that poses minimal risk is critical for safe navigation. People who are sighted and people who are visually impaired require navigation safety while walking on a sidewalk. In this research we developed an assistive navigation on a sidewalk by integrating sensory inputs using reinforcement learning. We trained a Sidewalk Obstacle Avoidance Agent (SOAA) through reinforcement learning in a simulated robotic environment. A Sidewalk Obstacle Conversational Agent (SOCA) is built by training a natural language conversation agent with real conversation data. The SOAA along with SOCA was integrated in a prototype device called augmented guide (AG). Empirical analysis showed that this prototype improved the obstacle avoidance experience about 5% from a base case of 81.29%

翻译：在这项研究中,我们利用强化学习整合了感官投入,在人行道上发展了辅助导航。我们通过在模拟机器人环境中强化学习,培训了人行道障碍避免代理(SOAA),通过培训具有真实对话数据的自然语言对话代理(SOCA),建立了一个人行道障碍对话代理(SOCA),培养了具有真实对话数据的自然语言对话代理(SOCA)。SOAA和SOCA被整合到一个称为扩展指南(AG)的原型装置中。经验分析显示,这一原型改善了在81.29基例中约5%的避免障碍经验。