FollowNet: Robot Navigation by Following Natural Language Directions with Deep Reinforcement Learning

Abstract-- Understanding and following directions provided by humans can enable robots to navigate effectively in unknown situations. FollowNet processes instructions using an attention mechanism conditioned on its visual and depth input to focus on the relevant parts of the command while performing the navigation task. Deep reinforcement learning (RL) a sparse reward learns simultaneously the state representation, the attention function, and control policies. We evaluate our agent on a dataset of complex natural language directions that guide the agent through a rich and realistic dataset of simulated homes. We show that the FollowNet agent learns to execute previously unseen instructions described with a similar vocabulary, and successfully navigates along paths not encountered during training. The agent shows 30% improvement over a baseline model without the attention mechanism, with 52% success rate at novel instructions. Humans often navigate unknown environments by observing their surroundings and following directions. These directions consist predominantly of landmarks and directional instructions and other common words.