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Learning provides a powerful tool for vision-based navigation, but thecapabilities of learning-based policies are constrained by limited trainingdata. If we could combine data from all available sources, including multiplekinds of robots, we could train more powerful navigation models. In this paper,we study how a general goal-conditioned model for vision-based navigation canbe trained on data obtained from many distinct but structurally similar robots,and enable broad generalization across environments and embodiments. We analyzethe necessary design decisions for effective data sharing across robots,including the use of temporal context and standardized action spaces, anddemonstrate that an omnipolicy trained from heterogeneous datasets outperformspolicies trained on any single dataset. We curate 60 hours of navigationtrajectories from 6 distinct robots, and deploy the trained GNM on a range ofnew robots, including an underactuated quadrotor. We find that training ondiverse data leads to robustness against degradation in sensing and actuation.Using a pre-trained navigation model with broad generalization capabilities canbootstrap applications on novel robots going forward, and we hope that the GNMrepresents a step in that direction. For more information on the datasets,code, and videos, please check out our project pagehttps://sites.google.com/view/drive-any-robot.

GNM: A General Navigation Model to Drive Any Robot