liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Model-Predictive Control with Stochastic Collision Avoidance using Bayesian Policy Optimization
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
2016 (English)In: IEEE International Conference on Robotics and Automation (ICRA), 2016, Institute of Electrical and Electronics Engineers (IEEE), 2016, 4597-4604 p.Conference paper, Published paper (Refereed)
Abstract [en]

Robots are increasingly expected to move out of the controlled environment of research labs and into populated streets and workplaces. Collision avoidance in such cluttered and dynamic environments is of increasing importance as robots gain more autonomy. However, efficient avoidance is fundamentally difficult since computing safe trajectories may require considering both dynamics and uncertainty. While heuristics are often used in practice, we take a holistic stochastic trajectory optimization perspective that merges both collision avoidance and control. We examine dynamic obstacles moving without prior coordination, like pedestrians or vehicles. We find that common stochastic simplifications lead to poor approximations when obstacle behavior is difficult to predict. We instead compute efficient approximations by drawing upon techniques from machine learning. We propose to combine policy search with model-predictive control. This allows us to use recent fast constrained model-predictive control solvers, while gaining the stochastic properties of policy-based methods. We exploit recent advances in Bayesian optimization to efficiently solve the resulting probabilistically-constrained policy optimization problems. Finally, we present a real-time implementation of an obstacle avoiding controller for a quadcopter. We demonstrate the results in simulation as well as with real flight experiments.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016. 4597-4604 p.
Series
Proceedings of IEEE International Conference on Robotics and Automation, ISSN 1050-4729
Keyword [en]
Robot Learning, Collision Avoidance, Robotics, Bayesian Optimization, Model Predictive Control
National Category
Robotics Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-126769DOI: 10.1109/ICRA.2016.7487661ISI: 000389516203138OAI: oai:DiVA.org:liu-126769DiVA: diva2:916711
Conference
IEEE International Conference on Robotics and Automation (ICRA), 2016, Stockholm, May 16-21
Projects
CADICSELLIITNFFP6CUASSHERPA
Funder
Linnaeus research environment CADICSELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsEU, FP7, Seventh Framework ProgrammeSwedish Foundation for Strategic Research
Available from: 2016-04-04 Created: 2016-04-04 Last updated: 2017-08-16Bibliographically approved
In thesis
1. Methods for Scalable and Safe Robot Learning
Open this publication in new window or tab >>Methods for Scalable and Safe Robot Learning
2017 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Robots are increasingly expected to go beyond controlled environments in laboratories and factories, to enter real-world public spaces and homes. However, robot behavior is still usually engineered for narrowly defined scenarios. To manually encode robot behavior that works within complex real world environments, such as busy work places or cluttered homes, can be a daunting task. In addition, such robots may require a high degree of autonomy to be practical, which imposes stringent requirements on safety and robustness. \setlength{\parindent}{2em}\setlength{\parskip}{0em}The aim of this thesis is to examine methods for automatically learning safe robot behavior, lowering the costs of synthesizing behavior for complex real-world situations. To avoid task-specific assumptions, we approach this from a data-driven machine learning perspective. The strength of machine learning is its generality, given sufficient data it can learn to approximate any task. However, being embodied agents in the real-world, robots pose a number of difficulties for machine learning. These include real-time requirements with limited computational resources, the cost and effort of operating and collecting data with real robots, as well as safety issues for both the robot and human bystanders.While machine learning is general by nature, overcoming the difficulties with real-world robots outlined above remains a challenge. In this thesis we look for a middle ground on robot learning, leveraging the strengths of both data-driven machine learning, as well as engineering techniques from robotics and control. This includes combing data-driven world models with fast techniques for planning motions under safety constraints, using machine learning to generalize such techniques to problems with high uncertainty, as well as using machine learning to find computationally efficient approximations for use on small embedded systems.We demonstrate such behavior synthesis techniques with real robots, solving a class of difficult dynamic collision avoidance problems under uncertainty, such as induced by the presence of humans without prior coordination. Initially using online planning offloaded to a desktop CPU, and ultimately as a deep neural network policy embedded on board a 7 quadcopter.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2017. 37 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1780
Keyword
Symbicloud, ELLIIT, WASP
National Category
Computer and Information Science Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-138398 (URN)10.3384/lic.diva-138398 (DOI)978-91-7685-490-7 (ISBN)
Presentation
2017-09-15, Alan Turing, E-huset, Campus Valla, Linköping, 10:15 (English)
Opponent
Supervisors
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsKnut and Alice Wallenberg FoundationSwedish Foundation for Strategic Research
Available from: 2017-08-17 Created: 2017-08-16 Last updated: 2017-08-18Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Andersson, OlovWzorek, MariuszRudol, PiotrDoherty, Patrick
By organisation
Artificial Intelligence and Integrated Computer SystemsFaculty of Science & Engineering
RoboticsComputer Science

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 1249 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf