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Course: 2023/2024

Intelligent decision-making in robotics

(19106)

Bachelor in Robotics Engineering (Plan: 478 - Estudio: 381)

Coordinating teacher: CASTRO GONZALEZ, ALVARO

Department assigned to the subject: Systems Engineering and Automation Department

Type: Compulsory

ECTS Credits: 3.0 ECTS

Course: 3º

Semester: 1º

Skills and learning outcomes

Link to document

Description of contents: programme

1. Introduction: autonomy in robotics, common terms, examples of applications, high-level vs. low-level decisions 2. Robotics paradigms: hierarchical, reactive, deliberative, hybrid 3. Dynamic Programming 4. Utility and Decision Theory 5. Game Theory 6. Probabilistic methods (Kalman filters, Particle filters, HMM, Dynamic Bayesian networks, POMDPs) 7. Reinforcement Learning 8. Bio-inspired Decision Making Systems

Learning activities and methodology

THEORETICAL PRACTICAL CLASSES. Knowledge and concepts students must acquire. Receive course notes and will have basic reference texts. Students partake in exercises to resolve practical problems. TUTORING SESSIONS. Individualized attendance (individual tutoring) or in-group (group tutoring) for students with a teacher. Subjects with 6 credits have 4 hours of tutoring/ 100% on- site attendance. STUDENT INDIVIDUAL WORK OR GROUP WORK. Subjects with 6 credits have 98 hours/0% on-site. WORKSHOPS AND LABORATORY SESSIONS. Subjects with 3 credits have 4 hours with 100% on-site instruction. Subjects with 6 credits have 8 hours/100% on-site instruction.

Assessment System

FINAL EXAM. 
Global assessment of knowledge, skills and capacities acquired throughout the course. The percentage of the evaluation varies for each subject between 60% and 0%.

CONTINUOUS EVALUATION. 
Assesses papers, projects, class presentations, debates, exercises, internships and workshops throughout the course. The percentage of the evaluation varies for each subject between 40% and 100% of the final grade.

% end-of-term-examination 60
% of continuous assessment (assigments, laboratory, practicals...) 40

Basic Bibliography

D.P. Bertsekas. Dynamic Programming and Optimal Control, Vols. I & II. Athena Press. 2017
B. Christian, T. Griffiths. Algorithms to Live By. William Collins Press. 2016
M. Mitzenmacher, E. Upfal. Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis. Cambridge University Press. 2017
R.R. Murphy. Introduction to AI Robotics. MIT Press. 2000

Additional Bibliography

Bertsekas and Tsitsiklis. Introduction to Probability. Athena Scientific.
Kochenderfer. Decision Making Under Uncertainty: Theory and Application. MIT Lincoln Laboratory Series.
Sutton and Barto. Reinforcement Learning: An Introduction. http://incompleteideas.net/sutton/book/the-book.html.

The course syllabus may change due academic events or other reasons.