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  4. Cognition for Telerobotics and Autonomous Systems

Cognition for Telerobotics and Autonomous Systems

Objectives

Understanding

  • Autonomous Systems basic concepts
  • Tele Operated Systems concepts
  • What are architectures and the different types that characterise autonomous systems
  • Context Awareness and Extraction
  • Application of Supervised and Unsupervised Learning to Robotics
  • Application of Deep Learning techniques to Robotics
  • The role of social implicit and explicit cues in robotics
  • Dynamic Task Planning and Scheduling
  • Mission Critical Planning
  • Multi-Robot Navigation and Planning

Able to Do

  • Addressing new problems and implementing strategies in the domain of robotized heterogeneous autonomous systems
  • Increase the capacity to practically implement robotized autonomous systems
  • Apply creativity and innovation

Non-Technical Competences

  • Develop synthesis critical thinking
  • Team working and increasing oral and writing communication skills
  • Improve time keeping and compliance with meeting deadlines

General characterization

Code

13090

Credits

6.0

Responsible teacher

José António Barata de Oliveira, Luís Manuel Camarinha de Matos

Hours

Weekly - Available soon

Total - 56

Teaching language

Português

Prerequisites

Knowledge of Robotics and Telerobotics.

Bibliography

 

  1.    Andrew, A. M. (1999). REINFORCEMENT LEARNING: AN INTRODUCTION by Richard S. Sutton and Andrew G. Barto, Adaptive Computation and Machine Learning series, MIT Press (Bradford Book), Cambridge, Mass., 1998, xviii+ 322 pp, ISBN 0-262-19398-1,(hardback,£ 31.95). Robotica, 17(2), 229-235.
  2.    Siegwart, R., Nourbakhsh, I. R., & Scaramuzza, D. (2011). Introduction to autonomous mobile robots. MIT press.
  3.    Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1, No. 2). Cambridge: MIT press.
  4.    Murphy, R. R. (2019). Introduction to AI robotics. MIT press.

Teaching method

Theoretical-practical classes (TP) are directed so that students, through their active participation, understand each of the topics listed in the learning objectives.

In laboratory classes (PL) students focus on the experimentation of the concepts exposed in theoretical-practical classes in order to know how to do.

For each practical work:

  • Presentation of the work,
  • tutorial on the technology / tools to use,
  • discussion of the work method,
  • realization of the work by the students accompanied by teachers, and
  • preparation of report.

Evaluation method

Evaluation Components

  1. 1.     2 Mini-Tests
  2. 2.     3 Practical Works

Evaluation Rules

  1. 1.     Theoretical Mark = (Mini-Test 1 + Mini-Test 2) / 2
  2. 2.     Theoretical Mark >= 9.5
  3. 3.     Each Practical Work >= 9.5
  4. 4.     Practical Mark = TP1 * Weight1 + TP2 * Weight2 + TP3*Peso3 ; Weights to be announced at the beginning of UC
  5. 5.     Final Mark = Practical Mark * 0.6 + Theoretical Mark * 0.4

Subject matter

1.       INTRODUCTION

a.       Intelligent Robots

b.       Brief History of AI ROBOTICS

c.       Automation and Autonomy

2.       SOFTWARE ORGANISATION OF AUTONOMY

a.       Introduction to Architectures

b.       Types of architectures

c.       Operational Deliberative and Reactive Architectures

d.       Operational Architecture

e.       Systems Architecture

3.       CONTEXTUAL AWARENESS

a.       Object and Scene Recognition

b.       3D Scene Understanding

c.       Action Recognition

4.       SUPERVISED AND UNSUPERVISED LEARNING IN ROBOTICS

5.       DEEP LEARNING APPLIED TO ROBOTICS

6.       HUMAN-ROBOT INTERACTION AND SOCIAL ROBOTICS

7.       MOBILE ROBOT AUTONOMY

a.       Task Scheduling

b.       Multi Criteria Scheduling

c.       Mission Critical Planning

8.       Multi-Robot Navigation, Coordination, and Planning

Programs

Programs where the course is taught: