Pathdreamer: A World Model for Indoor Navigation

Posted by Jing Yu Koh, Research Engineer and Peter Anderson, Senior Research Scientist, Google Research

When a person navigates around an unfamiliar building, they take advantage of many visual, spatial and semantic cues to help them efficiently reach their goal. For example, even in an unfamiliar house, if they see a dining area, they can make intelligent predictions about the likely location of the kitchen and lounge areas, and therefore the expected location of common household objects. For robotic agents, taking advantage of semantic cues and statistical regularities in novel buildings is challenging. A typical approach is to implicitly learn what these cues are, and how to use them for navigation tasks, in an end-to-end manner via model-free reinforcement learning. However, navigation cues learned in this way are expensive to learn, hard to inspect, and difficult to re-use in another agent without learning again from scratch.

People navigating in unfamiliar buildings can take advantage of visual, spatial and semantic cues to predict what’s around a corner. A computational model with this capability is a visual world model.

An appealing alternative for robotic navigation and planning agents is to use a world model to encapsulate rich and meaningful information about their surroundings, which enables an agent to make specific predictions about actionable outcomes within their environment. Such models have seen widespread interest in robotics, simulation, and reinforcement learning with impressive results, including finding the first known solution for a simulated 2D car racing task, and achieving human-level performance in Atari games. However, game environments are still relatively simple compared to the complexity and diversity of real-world environments.

In “Pathdreamer: A World Model for Indoor Navigation”, published at ICCV 2021, we present a world model that generates high-resolution 360º visual observations of areas of a building unseen by an agent, using only limited seed observations and a proposed navigation trajectory. As illustrated in the video

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