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A sensorimotor reinforcement learning framework for physical human-robot interaction
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0001-6738-9872
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0002-4266-6746
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0003-2965-2953
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2016 (English)In: IEEE International Conference on Intelligent Robots and Systems, IEEE, 2016, p. 2682-2688Conference paper, Published paper (Refereed)
Abstract [en]

Modeling of physical human-robot collaborations is generally a challenging problem due to the unpredictive nature of human behavior. To address this issue, we present a data-efficient reinforcement learning framework which enables a robot to learn how to collaborate with a human partner. The robot learns the task from its own sensorimotor experiences in an unsupervised manner. The uncertainty in the interaction is modeled using Gaussian processes (GP) to implement a forward model and an actionvalue function. Optimal action selection given the uncertain GP model is ensured by Bayesian optimization. We apply the framework to a scenario in which a human and a PR2 robot jointly control the ball position on a plank based on vision and force/torque data. Our experimental results show the suitability of the proposed method in terms of fast and data-efficient model learning, optimal action selection under uncertainty and equal role sharing between the partners.

Place, publisher, year, edition, pages
IEEE, 2016. p. 2682-2688
Keywords [en]
Behavioral research, Intelligent robots, Reinforcement learning, Robots, Bayesian optimization, Forward modeling, Gaussian process, Human behaviors, Human-robot collaboration, Model learning, Optimal actions, Physical human-robot interactions, Human robot interaction
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-202121DOI: 10.1109/IROS.2016.7759417ISI: 000391921702127Scopus ID: 2-s2.0-85006367922ISBN: 9781509037629 (print)OAI: oai:DiVA.org:kth-202121DiVA, id: diva2:1077669
Conference
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016, 9 October 2016 through 14 October 2016
Note

QC 20170228

Available from: 2017-02-28 Created: 2017-02-28 Last updated: 2018-05-21Bibliographically approved
In thesis
1. Sensorimotor Robot Policy Training using Reinforcement Learning
Open this publication in new window or tab >>Sensorimotor Robot Policy Training using Reinforcement Learning
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Robots are becoming more ubiquitous in our society and taking over many tasks that were previously considered as human hallmarks. Many of these tasks, e.g., autonomously driving a car, collaborating with humans in dynamic and changing working conditions and performing household chores, require human-level intelligence to perceive the world and to act appropriately. In this thesis, we pursue a different approach compared to classical methods that often construct a robot controller based on the perception-then-action paradigm. We devise robotic action-selection policies by considering action-selection and perception processes as being intertwined, emphasizing that perception comes prior to action and action is key to perception. The main hypothesis is that complex robotic behaviors come as the result of mastering sensorimotor contingencies (SMCs), i.e., regularities between motor actions and associated changes in sensory observations, where SMCs can be seen as building blocks to skillful behaviors. We elaborate and investigate this hypothesis by deliberate design of frameworks which enable policy training merely based on data experienced by a robot,without intervention of human experts for analytical modelings or calibrations. In such circumstances, action policies can be obtained by reinforcement learning (RL) paradigm by making exploratory action decisions and reinforcing patterns of SMCs that lead to reward events for a given task. However, the dimensionality of sensorimotor spaces, complex dynamics of physical tasks, sparseness of reward events, limited amount of data from real-robot experiments, ambiguities of crediting past decisions and safety issues, which arise from exploratory actions of a physical robot, pose challenges to obtain a policy based on data-driven methods alone. In this thesis, we introduce our contributions to deal with the aforementioned issues by devising learning frameworks which endow a robot with the ability to integrate sensorimotor data to obtain action-selection policies. The effectiveness of the proposed frameworks is demonstrated by evaluating the methods on a number of real robotic tasks and illustrating the suitability of the methods to acquire different skills, to make sequential action-decisions in high-dimensional sensorimotor spaces, with limited data and sparse rewards.

Abstract [sv]

Robotar förekommer alltmer i dagens samhälle och tar över många av de uppgifter som tidigare betraktades som tillägnade människor. Flera av dessa uppgifter, som att exempelvis autonomt köra en bil, samarbeta med människor i dynamiska och föränderliga arbetsmiljöer, samt att utföra sysslor i hemmet, kräver mänsklig intelligens för att roboten ska uppfatta världen och agera på lämpligt sätt. I denna avhandling utgår vi ifrån ett annat tillvägagångssätt jämfört med de klassiska metoder för skapande av robotsystem som tidigare ofta byggde på en så kallad perception-then-action paradigm. Vi utformar strategier för val av robotaktioner genom att utgå ifrån att det finns ett önsesidigt beroende mellan perception och aktion, där perception kommer före aktion, samtidigt som aktion är nödvändigt för perception. Huvudhypotesen är att komplexa robotbeteenden kommer som ett resultat av att roboten lär sig bemästra så kallade sensorimotorkopplingar (SMC), dvs regelbundenheter mellan motoriska aktioner och dess motsvarande förändringar i sensoriska observationer, där SMC:ar kan ses som byggblock för komplexa beteenden. Vi utarbetar och undersöker denna hypotes genom att avsiktligt utforma en handfull robotexperiment där en robots kunskaper helt förvärvas utifrån sensorimotoriska data, utan intervention av mänskliga experter för analytisk modellering eller kalibreringar. Under sådana omständigheter är så kallad reinforcement learning (RL) en lämplig paradigm för val av aktioner, en paradigm helt baserad på sensoriska data och utförda motoraktioner, utan krav på handgjorda representationer av världen på hög nivå. Denna paradigm kan utnyttjas för att generera utforskande rörelsemönster och förstärka de sensorimotorkopplingar som leder till framgång för i viss given uppgift. Det finns dock flera faktorer som kompicerar sådan rent datadriven inlärning av beteenden, såsom den sensorimotoriska datans höga dimensionalitet, den fysiska uppgiftens komplexa dynamik, bristen och tvetydigheten i de experiment som leder till positiva utfall, den begränsade mängd experiment som kan göras på en verklig robot och säkerhetsaspekter. De bidrag som introduceras i denna avhandling avser att hantera ovannämnda problem, genom att skapa ramverk för inlärning som gör det möjligt för en robot att integrera sensorimotordata för inlärning av stratieger för val av aktioner. De föreslagna ramverkens effektivitet demonsteras genom att utvärdera metoder på ett antal verkliga robotuppgifter och illustrera metodernas lämplighet för inlärning av olika färdigheter som kräver sekvenser av aktioner utifrån högdimensionell sensorimotorisk data, trots en begränsad mängd experiment med positivt utfall.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2018. p. 80
Series
TRITA-EECS-AVL ; 2018:47
Keywords
Reinforcement Learning, Artificial Intelligence, Robot Learning, Sensorimotor, Policy Training
National Category
Computer and Information Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-228295 (URN)978-91-7729-825-0 (ISBN)
Public defence
2018-06-11, F3, Lindstedtsvägen 26, Stockholm, 14:00 (English)
Opponent
Supervisors
Note

QC 20180521

Available from: 2018-05-21 Created: 2018-05-21 Last updated: 2018-05-21Bibliographically approved

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Publisher's full textScopushttp://www.iros2016.org/

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Ghadirzadeh, AliMaki, AtsutoKragic, DanicaBjörkman, Mårten

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