41118 Artificial Intelligence in Robotics
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Credit points: 6 cp
Subject level:
Undergraduate
Result type: Grade, no marksRequisite(s): 41013 Industrial Robotics AND 41012 Programming for Mechatronic Systems
Description
Intelligent robots are a disruptive technology, poised to transform business and society. However, developing intelligent robot behaviours is different to traditional business applications. Intelligent robots are real-time distributed systems that must make complex real-time decisions autonomously, using data collected from a wide range of sources such as sensors and the internet. To deal with this complexity, professionals must not only make sense of complexity, context and social norms in real-world scenarios, but translate such insights to algorithms suitable for autonomous use by a robot.
This subject helps students to understand and use concepts, theory, and algorithms of Artificial Intelligence (AI) related to robotics and general mechatronic systems. This includes areas such as data-driven perception systems, supervised, unsupervised, reinforcement, and deep learning for sensing and decision making, applied to intelligent robots. Through a series of workshops and hands-on tutorials, the students develop an appreciation for the complexity, technical challenges, and ethical issues associated with data-driven intelligent systems. They gain an ability to recognise and select state-of-the-art algorithms, methodologies, techniques, experimental tools and evaluation methods.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Design an intelligent robot solution for a real-world problem. (C.1) |
|---|---|
| 2. | Demonstrate Artificial Intelligence techniques in robotics. (D.1) |
| 3. | Demonstrate effective communication to present and document a robotic system solution. (E.1) |
| 4. | Critically review self and peer performance to ensure improvement of oneself and a team. (F.1) |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):
- Design Oriented: FEIT graduates apply problem solving, design and decision-making methodologies to develop components, systems and processes to meet specified requirements. (C.1)
- Technically Proficient: FEIT graduates apply abstraction, mathematics and discipline fundamentals, software, tools and techniques to evaluate, implement and operate systems. (D.1)
- Collaborative and Communicative: FEIT graduates work as an effective member or leader of diverse teams, communicating effectively and operating within cross-disciplinary and cross-cultural contexts in the workplace. (E.1)
- Reflective: FEIT graduates critically self-review their performance to improve themselves, their teams, and the broader community and society. (F.1)
Contribution to the development of graduate attributes
Engineers Australia Stage 1 Competencies
This subject contributes to the development of the following Engineers Australia Stage 1 Competencies:
- 1.2. Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
- 1.3. In-depth understanding of specialist bodies of knowledge within the engineering discipline.
- 2.2. Fluent application of engineering techniques, tools and resources.
- 2.3. Application of systematic engineering synthesis and design processes.
- 3.2. Effective oral and written communication in professional and lay domains.
- 3.4. Professional use and management of information.
- 3.5. Orderly management of self, and professional conduct.
Teaching and learning strategies
Modern teaching techniques will be used in this subject to facilitate active learning through case studies and practical exercises in clearly defined and real-life context. The students will further enhance the competencies in AI and machine learning required to develop intelligent robotic system. Students will have the opportunity to review learning materials, participate in in-class (online or face-to-face) discussions and to follow guided exercises to further strengthen their knowledge. Students will personalise their learning experience and methods by an open project on intelligent robotics system development and delivery.
Content (topics)
- Introduction to AI in Robotics
- Data-driven vs model-driven methods for Intelligent Robots
- Machine Learning for Robotic Perception
- Reinforcement Learning for Robotic Action and Planning
- Deep Learning for Robotics
- Legislative, ethical and social implications of AI in Robotics
- Evaluation and Benchmarking for AI in Robotics methods
Assessment
Assessment task 1: Mastery Quiz
| Intent: | The aim of this task is to assess basic?understanding?of the AI for Robotics algorithms.?? |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 2 and 3 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): D.1 and E.1 |
| Type: | Quiz/test |
| Groupwork: | Individual |
| Weight: | Mandatory task that does not contribute to subject mark |
| Length: | variable?? |
Assessment task 2: Project
| Intent: | All students wishing to apply technical knowledge to a real situation and achieve more than a pass for this subject must undertake a project, starting after week 7 of the subject. This is an individually and group-assessed project that contributes to the overall subject grade. Self and Peer Assessment will be undertaken as part of working collaboratively in teams. The result type for this subject is Grade no Mark. Specification grading based on UTS grading scheme for coursework subjects is adopted. Through the project the students will demonstrate their ability to develop and deliver AI algorithms to solve a real intelligent robot problem to an agreed scope. In doing so, students also will demonstrate their capacity to solve problems, create solutions, work with teams, communicate professionally, and manage time and tasks |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3 and 4 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1, D.1, E.1 and F.1 |
| Type: | Project |
| Groupwork: | Group, individually assessed |
| Weight: | Mandatory task that does not contribute to subject mark |
| Length: | In class and self-learning time |
Minimum requirements
In order to pass the subject, a student must achieve an overall mark of 50% or more.
Recommended texts
External online resources??