# Difference between revisions of "Zero-Shot Visual Imitation"

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===Forward Consistency Loss=== | ===Forward Consistency Loss=== |

## Revision as of 14:38, 3 November 2018

This page contains a summary of the paper "Zero-Shot Visual Imitation" by Pathak, D., Mahmoudieh, P., Luo, G., Agrawal, P. et al. It was published at the International Conference on Learning Representations (ICLR) in 2018.

## Contents

## Introduction

The dominant paradigm for imitation learning relies on strong supervision of expert actions to learn both *what* and *how* to imitate for a certain task. For example, in the robotics field, Learning from Demonstration (LfD) (Argall et al., 2009; Ng & Russell, 2000; Pomerleau, 1989; Schaal, 1999) requires an expert to manually move robot joints (kinesthetic teaching) or teleoperate the robot to teach a desired task. The expert will, in general, provide multiple demonstrations of a specific task at training time which the agent will form into observation-action pairs to then distill into a policy for performing the task. In the case of demonstrations for a robot, this heavily supervised process is tedious and unsustainable especially looking at the fact that new tasks need a set of new demonstrations for the robot to learn from.

*Observational Learning* (Bandura & Walters, 1977), a term from the field of psychology, suggests a more general formulation where the expert communicates *what* needs to be done (as opposed to *how* something is to be done) by providing observations of the desired world states via video or sequential images. This is the proposition of the paper and while this is a harder learning problem, it is possibly more useful because the expert can now distill a large number of tasks easily (and quickly) to the agent.

### Learning the Goal-Conditioned Skill Policy (GSP)

[math]S : \{x_1, a_1, x_2, a_2, ..., x_T\}[/math]

[math]a = π_E(s)[/math]

[math](x_i,x_g)[/math]

[math](\overrightarrow{a}_τ : a_1,a_2...a_K)[/math]

[math](x_g)[/math]

[math](x_i)[/math]