  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%
