| Unit name | Artificial Intelligence for Robotics |
|---|---|
| Unit code | SEMTM0016 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Miss. Lee |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
None |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Engineering Mathematics and Technology |
| Faculty | Faculty of Engineering |
Why is this unit important?
This unit will provide a broad introduction to artificial intelligence (AI) and methods of machine learning (ML) in a robotics context. It provides an overview of the most established robotic learning techniques and gives students the opportunity to implement AI algorithms and use relevant software tools. Learning is incredibly important in robotics as it allows a robot to adapt to changes in its world (such in the environment or task) or changes in itself (such as the impact of wear and tear or hardware malfunctions). This unit will enable students to design and build robotic systems which have the capacity to learn.
How does this unit fit into your programme of study
This unit is the first in the programme to develop your skills and knowledge in core artificial intelligence. In earlier units of study, you covered foundational robotics skills such as the design, control, modelling, and evaluation of robotic systems. This unit will extend these foundational skills to show how they can be applied in robot learning. The capacity to learn is what gives robots the ability to continually adapt their actions and improve performance over time, enabling us to build robots that can learn how to walk, run, fly and even play table tennis. For some students, the knowledge and skills gained in this unit will form the foundation of their dissertation work.
An overview of content
This unit provides an overview of the most established AI and robot learning methods and gives students the opportunity to implement AI algorithms and use relevant software tools. Areas covered will include supervised learning (classification and regression, e.g. neural networks), unsupervised learning (clustering and PCA), and reinforcement learning (e.g. Q-learning). The unit will also cover how to make appropriate learning method design choices and how to embed learning within the wider robot architecture impacting, for example, upon the robotic design, perception and control.
How will students, personally, be different as a result of the unit
At the end of this unit, students will be fluent in the terminology of AI and ML and understand the underpinnings of recent advances in these areas. Students will be equipped with the fundamental skills that they need to apply AI and ML to practical problems related to robotic systems and will have an appreciation for the strengths and limitations of AI and ML in robotics.
Learning outcomes
On successful completion of this unit, students will be able to:
Teaching will be delivered through a combination of synchronous and asynchronous sessions, including pre-recorded video lectures, on-campus lecture/Q&A sessions, and formative self-directed exercises. The unit will be supported by regular computer labs; these will provide student-centred on-campus learning through practical problem solving and will create a supportive environment where students apply for themselves the theory and methods discussed in the unit. Students will be expected to actively participate in the lectures and labs and to engage with videos, readings, self-directed exercises, and problem-solving activities.
Tasks which help you learn and prepare you for summative tasks (formative):
All computer lab sessions will have some formative tasks exercises with answers discussed through lab session engagement with teaching staff.
Lectures will provide examples and case studies that will be worked through in class: students are expected to use the solutions to these to improve their understanding.
Tasks which count towards your unit mark (summative):
Individual coursework project assessing all learning outcomes (100%).
Students will be posed a multi-faceted problem. Students will need to provide solutions with accompanying rationale/commentary on why the chosen methods were selected.
When assessment does not go to plan:
Re-assessment takes the same form as the original summative assessment.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. SEMTM0016).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
