Undergraduate Teaching 2017-18

Engineering Tripos Part IIB, 4M20: Robotics, 2017-18

Engineering Tripos Part IIB, 4M20: Robotics, 2017-18

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Dr F Iida


Dr F Iida, Prof R Cipolla, Dr A Rosendo

Timing and Structure

Michaelmas term, 100% coursework


3C5 useful; 3C8 useful; 3F2 useful; 3F3 useful


The aims of the course are to:

  • Introduce fundamentals of robotics
  • Case studies of practical applications such as robotic manipulation, locomotion and navigation
  • Hands-on exercises on robot development through projects
  • Presentation of research and development


As specific objectives, by the end of the course students should be able to:

  • Understand different design strategies and architectures of intelligent and autonomous machines
  • Design and integrate systems with basic components (actuators, sensors, controllers, and simulators)
  • Model and analyse kinematics and dynamics of robot systems


Introduction 2 Lectures (F. Iida)

- Landscape of robotics: Theories, technologies, applications and research areas

- Fundamentals of intelligent autonomous robots; Robotics and AI (Intelligence as search algorithms, Frame problem, Frame-of-reference problem, Grounding problem, Embodiment, DoF problem); Robotics and biology (Similarities and differences, Biological inspirations, Modeling of animals and machines, Case studies)

- The spectrum of robot architecture (Sense-Think-Act paradigm, Reflex based architecture, Behavior-based architecture, Passivity-based architecture)

- Introduction of research tools and areas (mainly for coursework)

Robot motion control 4 Lectures (F. Iida)

- Kinematic and dynamic control of robot motions (robotic arms, hands, wheels, legs)

- Underactuated robotics, passivity-based robot control, impedance control

- Simulation and analysis of robot motion and stability

Robot planning and navigation 2 Lectures (A. Rosendo)

- Theories and methods for planning of complex robot motions

- Theories and methods for robot navigation

Robot vision and perception  2 Lectures (R. Cipolla)

- Robot vision and robot sensors

- State-estimation, categorization, learning

Robot learning and autonomy 2 Lectures (F. Iida)

- Theories and methods of robot learning

- Case studies of robot learning and autonomy

Advanced topics and case studies 2 lectures (F. Iida and possibly other guest lecturers)

- Discussion of a few case studies out of advanced topics such as autonomous vehicle/UAV navigation, surgical/medical robotics, rehabilitation and healthcare robotics, industrial and service robotics, domestic robotics, human-robot interactions, bio-inspired robotics, computer vision and graphics  

Project presentation and competition 2 lectures (F. Iida)

- Students should present the simulation models of their robots and discuss outcome of the investigations


Each student will be assessed by the following three components of coursework:  

30%: Individual report to a problem set. The problem set consists of theoretical questions about robot control as well as some hands-on exercise on robot simulation. Details will be instructed in the first lecture. 

20%: Group presentation and robot competition. Students will work in a team of 2-4 people to develop and investigate their own manipulation/locomotion robots based on the kits provided. In the last week of the term, each team should give a 5-minute presentation and demonstrate the performance for competition. Details will be instructed in the first and second lectures.

50%: Individual dossier about the development and investigation of the projects. Each student should write a report about the project, and demonstrate how the theories and methods introduced in the lectures are used.  


Please see the Booklist for Group M Courses for references for this module.

Examination Guidelines

Please refer to Form & conduct of the examinations.

Last modified: 31/05/2017 10:00