Visual Goal-Directed Meta-Imitation Learning

Abstract

The goal of meta-learning is to generalize to new tasks and goals as quickly as possible. Ideally, we would like approaches that generalize to new goals and tasks on the first attempt. Requiring a policy to perform on a new task on the first attempt without even a single example trajectory is a zero-shot problem formulation. When tasks are identified by goal images, the tasks can be considered visually goal-directed. In this work, we explore the problem of visual goal-directed zero-shot meta-imitation learning. Inspired by several popular approaches to Meta-RL, we composed several core ideas related to task-embedding and planning by gradient descent to attempt to explore this problem. To evaluate these approaches, we adapted the Meta-world benchmark tasks to create 24 distinct visual goal-directed manipulation tasks. We found that 7 out of 24 tasks could be successfully completed on the first attempt by at least one of the approaches we tested. We demonstrated that goal-directed zero-shot approaches can translate to a physical robot with a demonstration based on Jenga block manipulation tasks using a Kinova Jaco robotic arm.