ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects
This work addresses standardization issues for researchers in embodied AI, but it is incremental as it consolidates existing practices rather than introducing new methods.
The paper tackles the problem of inconsistent interpretations in Object-Goal Navigation (ObjectNav) by establishing consensus recommendations for evaluation criteria, agent embodiment, and environment characteristics, resulting in a standardized framework implemented in CVPR 2020 challenges.
We revisit the problem of Object-Goal Navigation (ObjectNav). In its simplest form, ObjectNav is defined as the task of navigating to an object, specified by its label, in an unexplored environment. In particular, the agent is initialized at a random location and pose in an environment and asked to find an instance of an object category, e.g., find a chair, by navigating to it. As the community begins to show increased interest in semantic goal specification for navigation tasks, a number of different often-inconsistent interpretations of this task are emerging. This document summarizes the consensus recommendations of this working group on ObjectNav. In particular, we make recommendations on subtle but important details of evaluation criteria (for measuring success when navigating towards a target object), the agent's embodiment parameters, and the characteristics of the environments within which the task is carried out. Finally, we provide a detailed description of the instantiation of these recommendations in challenges organized at the Embodied AI workshop at CVPR 2020 http://embodied-ai.org .