Hierarchical Cross-Modal Agent for Robotics Vision-and-Language Navigation

Muhammad Zubair Irshad; Chih-Yao Ma; Zsolt Kira

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arXiv · 2104.10674

Hierarchical Cross-Modal Agent for Robotics Vision-and-Language Navigation

Muhammad Zubair Irshad·Chih-Yao Ma·Zsolt Kira

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Abstract

Deep Learning has revolutionized our ability to solve complex problems such as Vision-and-Language Navigation (VLN). This task requires the agent to navigate to a goal purely based on visual sensory inputs given natural language instructions. However, prior works formulate the problem as a navigation graph with a discrete action space. In this work, we lift the agent off the navigation graph and propose a more complex VLN setting in continuous 3D reconstructed environments. Our proposed setting, Robo-VLN, more closely mimics the challenges of real world navigation. Robo-VLN tasks have longer trajectory lengths, continuous action spaces, and challenges such as obstacles. We provide a suite of baselines inspired by state-of-the-art works in discrete VLN and show that they are less effective at this task. We further propose that decomposing the task into specialized high- and low-level policies can more effectively tackle this task. With extensive experiments, we show that by using layered decision making, modularized training, and decoupling reasoning and imitation, our proposed Hierarchical Cross-Modal (HCM) agent outperforms existing baselines in all key metrics and sets a new benchmark for Robo-VLN.

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BibTeX RIS

Muhammad Zubair Irshad, Chih-Yao Ma, Zsolt Kira. 2021-04-21. Hierarchical Cross-Modal Agent for Robotics Vision-and-Language Navigation. https://arxiv.org/abs/2104.10674

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