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Deep Learning Quadcopter Control via Risk-Aware Active Learning
Linköpings universitet, Institutionen för datavetenskap, Artificiell intelligens och integrerade datorsystem. Linköpings universitet, Tekniska fakulteten.
Linköpings universitet, Institutionen för datavetenskap, Artificiell intelligens och integrerade datorsystem. Linköpings universitet, Tekniska fakulteten.
Linköpings universitet, Institutionen för datavetenskap, Artificiell intelligens och integrerade datorsystem. Linköpings universitet, Tekniska fakulteten.
2017 (engelsk)Inngår i: Proceedings of The Thirty-first AAAI Conference on Artificial Intelligence (AAAI) / [ed] Satinder Singh and Shaul Markovitch, AAAI Press, 2017, Vol. 5, s. 3812-3818Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Modern optimization-based approaches to control increasingly allow automatic generation of complex behavior from only a model and an objective. Recent years has seen growing interest in fast solvers to also allow real-time operation on robots, but the computational cost of such trajectory optimization remains prohibitive for many applications. In this paper we examine a novel deep neural network approximation and validate it on a safe navigation problem with a real nano-quadcopter. As the risk of costly failures is a major concern with real robots, we propose a risk-aware resampling technique. Contrary to prior work this active learning approach is easy to use with existing solvers for trajectory optimization, as well as deep learning. We demonstrate the efficacy of the approach on a difficult collision avoidance problem with non-cooperative moving obstacles. Our findings indicate that the resulting neural network approximations are least 50 times faster than the trajectory optimizer while still satisfying the safety requirements. We demonstrate the potential of the approach by implementing a synthesized deep neural network policy on the nano-quadcopter microcontroller.

sted, utgiver, år, opplag, sider
AAAI Press, 2017. Vol. 5, s. 3812-3818
Serie
Proceedings of the AAAI Conference on Artificial Intelligence, ISSN 2159-5399, E-ISSN 2374-3468 ; 5
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-132800ISBN: 978-1-57735-784-1 (tryckt)OAI: oai:DiVA.org:liu-132800DiVA, id: diva2:1049877
Konferanse
Thirty-First AAAI Conference on Artificial Intelligence (AAAI), 2017, San Francisco, February 4–9.
Prosjekter
ELLIITCADICSNFFP6SYMBICLOUDCUGS
Forskningsfinansiär
Linnaeus research environment CADICSELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsEU, FP7, Seventh Framework ProgrammeCUGS (National Graduate School in Computer Science)Swedish Foundation for Strategic Research Tilgjengelig fra: 2016-11-25 Laget: 2016-11-25 Sist oppdatert: 2018-01-13bibliografisk kontrollert
Inngår i avhandling
1. Methods for Scalable and Safe Robot Learning
Åpne denne publikasjonen i ny fane eller vindu >>Methods for Scalable and Safe Robot Learning
2017 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Linköping: Linköping University Electronic Press, 2017. s. 37
Serie
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1780
Emneord
Symbicloud, ELLIIT, WASP
HSV kategori
Identifikatorer
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 (engelsk)
Opponent
Veileder
Forskningsfinansiär
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsKnut and Alice Wallenberg FoundationSwedish Foundation for Strategic Research
Tilgjengelig fra: 2017-08-17 Laget: 2017-08-16 Sist oppdatert: 2018-01-13bibliografisk kontrollert

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