Systems and methods for improving generalization in visual navigation

A method for training a trained model for use by a navigating robot to perform visual navigation, the method comprising:

• by one or more processors, generating N varied virtual training environments based on N base virtual training environments, respectively, by varying at least one characteristic of the respective N base virtual training environments,
• wherein each of the N base virtual training environments and each of the N varied virtual training environments includes a field of view at a location within an indoor space, and
• wherein N is an integer greater than 1;
• and
• by the one or more processors, training the trained model for use by the navigating robot to perform visual navigation based on a training set including:
• the N base virtual training environments;
• and
• the N varied virtual training environments,
• wherein the training includes:
• first training the trained model using reinforcement learning to optimize a proximal policy optimization (PPO) objective based on a reward set determined on whether or not a goal location is reached;
• and
• second training the trained model using regularization based on minimizing a penalty term that acts as a constraint on the PPO objective.

A navigating robot, comprising:

• a camera configured to capture images within a field of view in front of the navigating robot,
• wherein the field of view is less than 360 degrees around the navigating robot;
• a model that is:
• trained based on:
• N base virtual training environments at locations within an indoor space;
• and
• N varied virtual training environments generated by varying at least one characteristic of the N base virtual training environments, respectively,
• wherein N is an integer greater than 1, and
• the training including:
• first training the model using reinforcement learning to optimize a proximal policy optimization (PPO) objective based on a reward determined based on whether or not a goal location is reached;
• and
• second training the model using regularization based on minimizing a penalty term that acts as a constraint on the PPO objective;
• and
• configured to generate an output based on one of the images from the camera;
• one or more propulsion devices configured to propel the navigating robot;
• and
• a control module that actuates one or more of the one or more propulsion devices based on the output of the model.