Virtual Experience to Real World Application: Sidewalk Obstacle Avoidance Using Reinforcement Learning for Visually Impaired
This work addresses navigation safety for visually impaired people, but it appears incremental as it builds on existing methods with a modest performance gain.
The researchers tackled sidewalk navigation safety for visually impaired individuals by developing an assistive device that integrates reinforcement learning for obstacle avoidance and a conversational agent, resulting in a 5% improvement in obstacle avoidance experience from a baseline of 81.29%.
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%