Visual Semantic Planning Using Deep Successor Representations
Yuke Zhu, Daniel Gordon, Eric Kolve, Dieter Fox, Li Fei-Fei, Abhinav Gupta, Roozbeh Mottaghi, Ali Farhadi
Abstract
A crucial capability of real-world intelligent agents is their ability to plan a sequence of actions to achieve their goals in the visual world. In this work, we address the problem of visual semantic planning: the task of predicting a sequence of actions from visual observations that transform a dynamic environment from an initial state to a goal state. Doing so entails knowledge about objects and their affordances, as well as actions and their preconditions and effects. We propose learning these through interacting with a visual and dynamic environment. Our proposed solution involves bootstrapping reinforcement learning with imitation learning. To ensure cross task generalization, we develop a deep predictive model based on successor representations. Our experimental results show near optimal results across a wide range of tasks in the challenging THOR environment. The supplementary video can be accessed at the following link: https://goo.gl/vXsbQP.
BibTeX
@inproceedings{iccv2017_visualsemanticpl,
title = {Visual Semantic Planning Using Deep Successor Representations},
author = {Yuke Zhu and Daniel Gordon and Eric Kolve and Dieter Fox and Li Fei-Fei and Abhinav Gupta and Roozbeh Mottaghi and Ali Farhadi},
booktitle = {ICCV 2017},
year = {2017}
}