Differential dynamic programming for graph-structured dynamical systems: Generalization of pouring behavior with different skills

Akihiko Yamaguchi, Christopher G. Atkeson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

We explore differential dynamic programming for dynamical systems that form a directed graph structure. This planning method is applicable to complicated tasks where sub-tasks are sequentially connected and different skills are selected according to the situation. A pouring task is an example: it involves grasping and moving a container, and selection of skills, e.g. tipping and shaking. Our method can handle these situations; we plan the continuous parameters of each subtask and skill, as well as select skills. Our method is based on stochastic differential dynamic programming. We use stochastic neural networks to learn dynamical systems when they are unknown. Our method is a form of reinforcement learning. On the other hand, we use ideas from artificial intelligence, such as graph-structured dynamical systems, and frame-and-slots to represent a large state-action vector. This work is a partial unification of these different fields. We demonstrate our method in a simulated pouring task, where we show that our method generalizes over material property and container shape. Accompanying video: https://youtu.be/-ECmnG2BLE8.

Original languageEnglish
Title of host publicationHumanoids 2016 - IEEE-RAS International Conference on Humanoid Robots
PublisherIEEE Computer Society
Pages1029-1036
Number of pages8
ISBN (Electronic)9781509047185
DOIs
Publication statusPublished - 2016 Dec 30
Externally publishedYes
Event16th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2016 - Cancun, Mexico
Duration: 2016 Nov 152016 Nov 17

Publication series

NameIEEE-RAS International Conference on Humanoid Robots
ISSN (Print)2164-0572
ISSN (Electronic)2164-0580

Other

Other16th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2016
CountryMexico
CityCancun
Period16/11/1516/11/17

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Human-Computer Interaction
  • Electrical and Electronic Engineering

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