Predictive models and navigation

Zero-Shot Agent Navigation with Hindsight Experience Replay (2020-2022)

We will extend the Hindsight Experience Replay concept from manipulation of objects to navigation of a mobile robot Agent. We will also attempt to use policy learning models that can function in correlated-online-sampling conditions, so that a single mobile Agent can learn from its individual experiences. Combined with our Episodic memory system for fast few-shot or even one-shot learning, the Agent will be able to adapt rapidly to a continually changing environment. Finally, by adding the ability to plan in a mental simulation, the Agent should demonstrate zero-shot navigation – successfully planning & executing a path it has never experienced. The project is currently paused until we have a larger team.

Predictive Generative Models (2019-2020)

This project aims to develop a predictive model – such as our RSM – into a self-looping generative model that could be used to create simulations of future events. We’ve begun testing the model on the “bouncing balls” task described in “Predictive Generative Networks” (Lotter et al 2015), and RAVDESS, a database of actors singing, speaking and emoting.

This project was a crucial step towards our long-term goal of developing neural models of mental simulation.