Human-Robot System Engineering

NUS-ISS

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Objectives

- Identify and evaluate a business case for pitching a social and humanised robotic project;- Integrate human-robot interaction modules using concepts in mental transformation, cognitive loading, Augmented//mixed reality and teleoperations- Evaluate technical considerations, feasibility and implications of integrating multiple systems and components according to the integration strategy- Manage the outcome of systems integration by utilising various front- and back-end components for human-robot interactions, for example, speech recognition, facial detection, gesture recognition, touch-screen, proprioception, eye-hand coordination, 3D tracking and scanning;- Develop a high-level view of robotic interfacing modules in the various areas such as industrial, healthcare, social and security applications;- Identify the types of techniques and platforms for a particular automation requirement in social, aesthetic and operational contexts;- Acquire emotional facial expression decoding skills with expressive simulation software- Design an integration roadmap driven by user experience for creating socially interactive robotics.



Who Should Attend

Decision-makers, senior/mid-senior executives and engineers involved in the process to design programmes meant to deploy and trial social robot systems or directly involved in the process to build and operate robots and RPAs in various application areas, particularly those that involve substantial interactions with humans.




Entry Requirements

- Participants should be familiar with some Python and/or ROS programming.- Participants should be familiar with some fundamental knowledge of robotics. Participants new to robotics may consider attending Robotics Systems




Class Schedule

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Tutorial Schedule

NA




Lesson Delivery

NA




Full Fees (before GST)

S$3600.00




Nett Fees payable after SSG Funding



3531.60


Last updated: 04 June 2024

Upcoming Course Dates
TBC

Delivery Mode**
Face-to-Face

Course Code
HRSE-NF

Funding Type
SSG Funding
Eligible for SkillsFuture Credit (SFC)
Alumni eVoucher (L3)

Type
Short Course

Stacks/ Bundles to
NA

Audit/Graded
Graded

Area of Interest
AI & Machine Learning