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Jožef Stefan
International
Postgraduate School

Jamova 39
SI-1000 Ljubljana
Slovenia

Phone: +386 1 477 31 00
Fax: +386 1 477 31 10
Email: info@mps.si

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Course Description

Humanoid and Service Robotics

Program

Information and Communication Technologies, third-level study programme

Lecturers:

doc. dr. Bojan Nemec
doc. dr. Andrej Gams

Goals:

The objective of this course to obtain theoretical and practical knowledge of the basics of service and humanoid robotics, control, learning and applications of service and humanoid robots. The emphasis is on modern approaches of the integration of robot systems into human-like environments.

The obtained knowledge will allow the students to understand the basic principles of motion and handle modern technologies of service robotics and to apply these technologies into real practice.

Content:

Basic structure of humanoid and service robots

Parametric policy representation
Discrete and periodic policies,
Representations using Hidden Markov model, Gaussian mixture model, Gaussian mixture regression, Dynamic motion primitives, Probability motion primitives, Interactive motion primitives, Compliant motion primitives

Learning for humanoid and service robots:
Imitation learning
Reinforcement learning

Motion generalization:
Statistical generalization methods

Autonomous motion adaptation:
Using iterative learning control
Using reinforcement learning
Using neural networks

Robot learning and adaptation in latent spaces:
Latent space policy representation
Learning and task execution in latent spaces

Optimal robot control:
Using linear quadratic regulator
Extension to non-linear robot dynamics
Model predictive control in robotics

Humanoid and service robots in human environments:
Human – robot cooperation
Physical human – robot and robot – environment interaction
Motion synchronization and adaptation

Bimanual robot control:
Master-slave approach
Symmetrical task decomposition

Locomotion

Navigation:
SLAM
Indoor navigation
Outdoor navigation

Advanced sensory systems for environment detection and localization:
RGBD cameras
Laser scanners
Proximity sensors

Service robot applications

Course literature:

Selected chapters from the following books:

• Siciliano, B., and Khatib, O. (eds.) Springer Handbook of Robotics, Springer-Verlag Berlin Heidelberg, 2016. ISBN 978-3-319-32552-1
• Corke, P. Field and Service Robotics, Springer, 2006. ISBN 10 3-540-33452-1
• Calinon, S. Robot Programming by Demonstration, EPFL Press 2009, ISBN-13: 978-1439808672
• Vadakkepat, P. and Goswami, A. (eds.) Humanoid Robotics: A Reference, Springer, 2017, ISBN 978-94-007-6045-5
• Haddadin, S.: Towards Safe Robots, Springer Berlin Heidelberg, 2014
• Kober, J. and Peters, J. Learning Motor Skills from Algorithms to Robot Experiments. Heidelberg: Springer-Verlag, 2014. ISBN 978-3-319-03193-4
• Nemec, B., and Ude, A. Robot skill acquisition by demonstration and explorative learning, In New Trends in Medical and Service Robotics, Springer 2014, ISBN 978-3-319-05431-8
• Calinon, S. A Tutorial on Task-Parameterized Movement Learning and Retrieval, Intelligent Service Robotics (Springer), 9:1, 1-29, 2016.
• Herzog, A., Rotella, N., Mason, S., Grimminger, S., Schaal, S., Righetti, L. Momentum control with hierarchical inverse dynamics on a torque-controlled humanoid, Autonomous Robot 40: 473, 2016.
• Chen, N., Karl, M., van der Smagt, P. Dynamic Movement Primitives in Latent Space of Time-Dependent Variational Autoencoders. Proc. 16th IEEE-RAS International Conference on Humanoid Robots, 2016.

Significant publications and references:

• M. Tamosiumaite, B. Nemec, A. Ude, F. Wφrgφtter. Learning to pour with a robot arm combining goal and shape learning for dynamic movement primitives. Robot. auton. Syst, 59 (11), 910-922, 2011.
• A. Gams, A. J. Ijspeert, S. Schaal, J. Lenarčič. On-line learning and modulation of periodic movements with nonlinear dynamical systems. Autonomous robots, 27(1), 3-23, 2009.
• A. Ude, A. Gams, T. Asfour, J. Morimoto. Task-specific generalization of discrete and periodic dynamic movement primitives. IEEE transactions on robotics 26(5), 800-815, 2010.
• B. Nemec, A. Ude. Action sequencing using dynamic movement primitives. Robotica, 5 (30), 837-846, 2012.
• B. Nemec, R. Vuga, A. Ude. Efficient sensorimotor learning from multiple demonstrations. Advanced robotics, 3 (27), 1023-1031, 2013
• T. Petrič, T. L. Peternel, A. Gams, B. Nemec, L. Žlajpah. Navigation methods for the skiing robot. International journal of humanoid robotics, 4 (10), 1350029-1-1350029-21, 2013
• A. Gams, B. Nemec, A. J. Ijspeert, A. Ude. Coupling movement primitives : interaction with the environment and bimanual tasks. IEEE transactions on robotics, 30 (49), 816-830, 2014.
• R. Vuga, B. Nemec, A. Ude. Speed adaptation for self-improvement of skills learned from user demonstrations. Robotica, 34(12), 2806-2822, 2016
• A. Gams, T. Petrič, M. Do, B. Nemec, J. Morimoto, T. Asfour, A. Ude. Adaptation and coaching of periodic motion primitives through physical and visual interaction. Robotics and autonomous systems, 75,340-351, 2016
• F. Abu Dakka, B. Nemec, J. Jorgensen, T.R. Savarimuthu, N. Krόger, A. Ude. Adaptation of manipulation skills in physical contact with the environment to reference force profiles. Autonomous robots, 39 (2),199-217, 2016
• M. Deniša, A. Gams, A. Ude, T. Petrič. Learning compliant movement primitives through demonstration and statistical generalization. IEEE/ASME transactions on mechatronics, 21(5), 2581-2594, 2016

Examination:

Oral exam (50%)
Seminar work with oral defense (50%)

Students obligations:

Oral exam
Seminar work with oral defense

Links: