Look Before You Leap: Bridging Model Free and Model Based Reinforcement Learning for Planned Ahead Vision and Language Navigation
Existing research studies on vision and language grounding for robot navigation focus on improving model free deep reinforcement learning (DRL) models in synthetic environments. However, model free DRL models do not consider the dynamics in the real world environments, and they often fail to generalize to new scenes. In this paper, we take a radical approach to bridge the gap between synthetic studies and real world practices We propose a novel, planned ahead hybrid reinforcement learning model that combines model free and model based reinforcement learning to solve a real world vision language navigation task. Our look ahead module tightly integrates a look ahead policy model with an environment model that predicts the next state and the reward. Experimental results suggest that our proposed method significantly outperforms the baselines and achieves the best on the real world Room to Room dataset. Moreover, our scalable method is more generalizable when transferring to unseen environments, and the relative success rate is increased by 15.5% on the unseen test set.
ECCV