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This is an archived syllabus from 2019-2020

COMP34212 Cognitive Robotics syllabus 2019-2020

COMP34212 Cognitive Robotics

Level 3
Credits: 10
Enrolled students: 67

Course leader: Angelo Cangelosi

Additional staff: view all staff


  • Pre-Requisite (Compulsory): COMP24111

Assessment methods

  • 70% Written exam
  • 30% Report
Sem 2 Lecture 1.4 Fri 10:00 - 11:00 -
Sem 2 Lecture 1.4 Mon 14:00 - 15:00 -
Sem 2 w23-26 Lab 1.8 Tue 11:00 - 13:00 -


The course will provide an introduction to the methods and software/hardware technologies for robotics. It will analyse the selection and application of AI and machine learning methods, such as deep learning, for designing intelligent behaviour and cognitive skills (e.g. vision, motor control, language, social skills). It will discuss a series of cognitive robotics models and experiments to understand how our knowledge of animal and human cognition and neuroscience can inform the development of intelligent skills in robots. The course will also discuss the role of ethics and responsible research and innovation in robotics research and applications.


This unit provides an in-depth understanding of the field of cognitive robotics. This will analyse the selection, use and combination of methods and approaches in robotics, in artificial intelligence and in psychology and neuroscience to design intelligent behaviour and cognitive skills in interactive robots.


Lecture topics:

  • Introduction to Cognitive Robotics
  • Overview of robot technologies, sensors and actuators
  • Robot platforms
  • Machine learning for robotics
  • Developmental Robotics
  • Neuro-robotics
  • Evolutionary and swarm robotics
  • Social robotics and human-robot interaction
  • Language learning and speech interfaces
  • Robot tutors for children
  • Ethics for robotics and AI

Practical Labs:

The practical lab sessions will focus on the use of machine learning methods, such as deep learning, for robot vision and language and on the software tools for robotics.

Teaching methods


24 in total, 2 per week

Labs with TA support plus coursework and exam preparation and independent study

Feedback methods

Feedback on report and additional oral feedback during office/surgery hours and during labs.

Study hours

  • Assessment written exam (2 hours)
  • Lectures (24 hours)
  • Practical classes & workshops (8 hours)

Practical skills

Aim and Deliverable

The aim of this coursework is to develop skills on the design, execution and evaluation of deep neural networks experiments for robotics. It also aims at discussing the role of the deep learning approach within the context of the state of the art in robotics. The assignment will in particular address the learning outcome LO1 on the analysis of the methods and software technologies for robotics, and LO3 on applying different machine learning methods for intelligent behaviour.

Your task is to extend the deep learning laboratory exercises (e.g. Multi-Layer Perceptron (MLP) and/or Convolutional Neural Network (CNN) exercises for image datasets) and carry out and analyse new training simulations. This will allow you to evaluate the role of different hyperparameter values and explain and interpret the general pattern of results to optimise the training for robotics (vision) applications. You should also contextualised yoru work within the state of the art, with a discussion of the role of deep learning and its pros and cons for robotics research and applications.

You can use the standard object recognition datasets (e.g. CIFAR, COCO) or robotics vision datasets (e.g. iCub World[1], RGB-D Object Dataset[2])

The deliverable to submit is a report (5 pages including figures/tables) to describe and discuss the training simulations done and their context within robotics research and applications.

Marking Criteria (out of 30)

A clear introductory to the problem and the methodology to be used, with justification of the network topology and hyperparameters chosen [3]

  1. Contextualisation of deep learning methodologies within the state of the art of deep learning for robotics [8]
  2. Complexity of the network(s), hyperparameters and dataset [7]
  3. Description, interpretation and assessment of the results on the hyperparameter testing simulations, including appropriate figures and tables to support the results. [12]

Due Date: 184.00 on 20 April 2020, uploaded to BlackBoard as a PDF. Use standard file name: 34212-Lab-S-Report

Learning outcomes

On successful completion of this unit, a student will be able to:

At the end of this course a student will be able to:

1. analyse the methods and software/hardware technologies for robotics research and applications

2. understand how our psychology and neuroscience understanding of behaviour and intelligence informs the design of robotics models and applications

3. compare, select and apply different machine learning methods for intelligent behaviour in robots

4. Discuss the state of the art in cognitive and intelligent robotics models, and how this informs the design of future robot applications

5. Discuss the role of ethics and responsible research and innovation in robotics



Reading list

Developmental robotics from babies to robots Cangelosi, Angelo, 1967- author.9780262325301The MIT Press2015
Deep learning Goodfellow, Ian, author.9780262035613MIT Press2016
Artificial cognitive systems : a primer Vernon, David,9780262326698The MIT Press[2014]