Programme outline
Learning objectives
By the end of this course, participants will be able to:
- Define Physical Artificial Intelligence (AI) and explain how it differs from generative and digital AI, and differentiate between embodied AI and classical robotics/automation.
- Describe the key building blocks of modern Physical AI systems: foundation models, multimodal sensing, simulation and synthetic data, and the perception action loop.
- Identify the main classes of physical agents (manipulators, mobile robots, autonomous vehicles, intelligent industrial systems) and the sectors where they are being deployed.
- Assess the compute, data and system integration infrastructure needed to develop and operate Physical AI at scale.
- Evaluate where Physical AI is most likely to create value or introduce risk in their own industry.
- Recognise the main ethics, safety and trustworthiness considerations of deploying autonomous physical systems.
- Engage credibly with technical teams, vendors and research partners on Physical AI topics.
Day 1
- Overview of Physical AI – sharing by SUTD and industry/government partner
- Focus Topic 1: Robotics Foundational Models & Architecture
- Focus Topic 2: Embodiments & Physical Agents
- Focus Topic 3: Building Infrastructure & System of System Technologies
- Focus Topic 4: Domain-Specific Physical AI
- Focus Topic 5: Ethics & Trustworthiness
- Course evaluation / end of course
Assessment
- Class participation
- Team discussion
- Presentation