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.

本文提出了一种新颖的，提前计划的混合增强学习模型，将模型无关的和模型基于的强化学习相结合，以解决实际的视觉语言导航任务，并且实验结果表明，该方法在真实数据集上表现最佳，还具有更好的可扩展性。

先斩后奏：桥接基于模型和基于模型的无模型强化学习，为规划先进的视觉语言导航