Učni načrt predmeta

Predmet:

Humanoidna in servisna robotika

Course:

Humanoid and Service Robotics

Študijski program in stopnja / Study programme and level	Študijska smer / Study field	Letnik / Academic year	Semester / Semester
Informacijske in komunikacijske tehnologije, 3. stopnja	Inteligentni sistemi in robotika	1	1
Information and Communication Technologies, 3rd cycle	Intelligent Systems and Robotics	1	1

Vrsta predmeta / Course type

Izbirni / Elective

Univerzitetna koda predmeta / University course code:

IKT3-623

Predavanja Lectures	Seminar Seminar	Vaje Tutorial	Klinične vaje work	Druge oblike študija	Samost. delo Individ. work	ECTS
30	30			30	210	10

*Navedena porazdelitev ur velja, če je vpisanih vsaj 15 študentov. Drugače se obseg izvedbe kontaktnih ur sorazmerno zmanjša in prenese v samostojno delo. / This distribution of hours is valid if at least 15 students are enrolled. Otherwise the contact hours are linearly reduced and transfered to individual work.

Nosilec predmeta / Course leader:

izr. prof. dr. Bojan Nemec

Sodelavci / Lecturers:

izr. prof. dr. Andrej Gams

Jeziki / Languages:

Predavanja / Lectures:

Slovenščina, angleščina / Slovenian, English

Vaje / Tutorial:

Pogoji za vključitev v delo oz. za opravljanje študijskih obveznosti:

Prerequisites:

Zaključena druga stopnja bolonjskega študija ali diploma univerzitetnega študijskega programa. Pri tem predmetu je potrebno predznanje matematike, fizike, znanje o sistemih vodenja in programiranja.

Priporočeni predmeti:
- inteligentni sistemi vodenja robotov
- robotski vid

Completed Bologna second-cycle study program or an equivalent pre-Bologna university study program. This course requires profound knowledge of mathematics, physics, theory of control systems and computer programming.

Recommended courses:
- Intelligent robot control
- Robot vision

Vsebina:

Content (Syllabus outline):

Osnovne značilnosti humanoidnih in servisnih robotov

Predstavitev nalog v parametrični obliki:
- Diskretne in periodične naloge,
- Predstavitve s skritimi markovskimi modeli, skupek Gaussovih porazdelitev, mešana Gaussova regresija, dinamični generatorji giba, verjetnostni generatorji giba, interaktivni generatorji giba, podajni generatorji giba.

Učenje robotov:-
- s posnemanjem
- spodbujevano učenje
- s prenosom znanja
- z uporabo velikih jezikovnih modelov

Posploševanje gibanja:
- Statistične metode posploševanja

Avtonomno prilagajanje gibanja:
- z uporabo učečih regulatorjev
- z uporabo podajnega vodenja
- z uporabo (globekega) spodbujevanega učenja
- z uporabo nevronskih mrež

Učenje in prilagajanje robotskih nalog v latentnih prostorih:
- zapis nalog v latentnih prostorih
- učenje in izvajanje gibanja v latentnih prostorih

Optimalno vodenje robotov:
- optimalni regulator za linearne sisteme
- razširitev optimalnega vodenja za nelinearne sisteme
- prediktivno vodenje s pomočjo modelov

Humanoidni in servisni roboti v človekovem okolju:
- sodelovanje človeka z robotom
- fizična interakcija človek – robot in robot – okolje
- sinhronizacija gibanja

Dvoročna manipulacija:
- pristop vodilni - sledilni
- pristop s simetrično dekompozicijo nalog

Uporaba senzorskih sistemi za zaznavo okolja:
- RGBD kamere
- laserski detektorji
- detektorji bližine

Primeri praktične uporabe algoritmov v servisni robotiki

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
- Transfer Learning
- using large language models

Motion generalization:
- Statistical generalization methods

Autonomous motion adaptation:
- Using iterative learning control
- Using compliant control
- Using (deep) 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
- Leader - follower approach
- Symmetric task decomposition

Use of advanced sensory systems for environment detection and localization:
- RGBD cameras
- Laser scanners
- Proximity sensors

Examples of practical used of algorithms in service robotics

Temeljna literatura in viri / Readings:

Izbrana poglavja iz naslednjih knjig: / 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.
- Jaquier, Noémie, Michael C. Welle, Andrej Gams, Kunpeng Yao, Bernardo Fichera, Aude Billard, Aleš Ude, Tamim Asfour, and Danica Kragić. "Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges." arXiv preprint arXiv:2311.18044 (2023), to appear in The International Journal of Robotics Research.

Cilji in kompetence:

Objectives and competences:

Cilj predmeta je osvojiti znanja iz osnov humanoidne in servisne robotike, vodenja, učenja ter uporabe humanoidnih in servisnih robotov. Poudarek je na sodobnih pristopih vključevanja robotskih mehanizmov v človekovo okolje.

Pridobljena znanja bodo omogočila študentom razumevanje principov gibanja in obvladovanje osnov sodobnih tehnologij s področja servisne robotike ter prenos teh tehnologij v prakso.

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.

Predvideni študijski rezultati:

Intendeded learning outcomes:

Študenti bodo z uspešno opravljenimi obveznostmi tega predmeta pridobili:
- razumevanje pomena in strukture humanoidnih in servisnih robotov;
- poznavanje vrste servisnih robotov, razlikovanje med servisnimi in humanoidnimi roboti, poznavanje njihovih značilnosti in tipičnih področji uporabe servisnih ter humanoidnih robotov,
- razumevanje sodobnih oblik zapisov trajektorij gibanja,
- razumevanje pomena ter principov avtonomne adaptacije gibanja robotov,
- razumevanje principov vodenja z uporabo generatorjev gibov oz. z optimizacijo,
- razumevanje osnov zapisa in izvajanja gibanja v latentnem prostoru,
- razumevanje sistemov navigacije, vodenja in učenja z demonstracijo,
- razumevanje pomena uporabe kompleksnih senzorskih sistemov v robotskih sistemih in razumevanje razlogov za uvajanje servisnih robotov ter razlogov za uvajanje humanoidnih robotov.

Students successfully completing this course will acquire:
- understanding of the structure and the aim of humanoid and service robots;
- knowledge of main characteristics of the various types of service robots and knowledge of the most common areas of applications for service robots and reasons for application of humanoid robots,
- understanding of contemporary form of encoding trajectories of motion
- understanding of principles of autonomous motion adaptation,
- understanding of control principles using motion primitives and optimization
- understand the basics of encoding and executing motion in latent spaces
- understanding of navigation, control and programming by demonstration principles,
- understanding of the importance of the complex sensory system in robotics, and knowledge of limitation and motivations for application of service and humanoid robots.

Metode poučevanja in učenja:

Learning and teaching methods:

Predavanja, seminar, konzultacije, individualno delo

Lectures, seminar, consultations, individual work

Načini ocenjevanja:

Delež v % / Weight in %

Assesment:

Ustni izpit

50 %

Oral exam

Seminarska naloga

25 %

Seminar work

Ustni zagovor

25 %

Oral defense

Reference nosilca / Lecturer's references:

1.	SIMONIČ, Mihael, UDE, Aleš, NEMEC, Bojan. Hierarchical learning of robotic contact policies. Robotics and computer-integrated manufacturing. Apr. 2024, vol. 86, 1-12 str., ilustr. ISSN 1879-2537
2.	SIMONIČ, Mihael, MAJCEN HROVAT, Matevž, DŽEROSKI, Sašo, UDE, Aleš, NEMEC, Bojan. Determining exception context in assembly operations from multimodal data. Sensors. 2022, vol. 22, no. 20, str. 7962-1-7962-20. ISSN 1424-8220
3.	NEMEC, Bojan, YASUDA, Kenichi, UDE, Aleš. A virtual mechanism approach for exploiting functional redundancy in finishing operations. IEEE transactions on automation science and engineering. [Print ed.]. 2021, vol. 18, no. 4, str. 2048-2060. ISSN 1545-5955
4.	Jaquier, Noémie, Michael C. Welle, Andrej Gams, Kunpeng Yao, Bernardo Fichera, Aude Billard, Aleš Ude, Tamim Asfour, and Danica Kragić. "Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges." arXiv preprint arXiv:2311.18044 (2023), to appear in The International Journal of Robotics Research.
5.	GAMS, Andrej, PETRIČ, Tadej, NEMEC, Bojan, UDE, Aleš. Manipulation learning on humanoid robots. Current robotics reports. 2022, vol. 3, str. 97-109. ISSN 2662-4087