Attention: Classroom assignments may change between the time you
register and when classes begin. Please check your class schedule for the latest classroom location
information before attending class.
ROBOT 5100 - 001 Robotics II: Control
Pre-reqs required: (ROBOT 5000/ ME EN 5220/ ECE 5650/ CS 5310) AND (ME EN 3220/ ME EN 5200/ ECE 5615). To enroll, fill out permission request form at https://admin.coe.utah.edu/permission-codes/student/request/robotics
ROBOT 5100 - 001 Robotics II: Control
- Class Number: 11136
- Instructor: MASCARO, STEPHEN
- Component: Lecture
- Type: In Person
- Units: 3.0
- Wait List: Yes
- Fees: $40.00
- Seats Available: 4
Pre-reqs required: (ROBOT 5000/ ME EN 5220/ ECE 5650/ CS 5310) AND (ME EN 3220/ ME EN 5200/ ECE 5615). To enroll, fill out permission request form at https://admin.coe.utah.edu/permission-codes/student/request/robotics
ROBOT 5800 - 001 Robotics Undergrad Sem
ROBOT 5800 - 001 Robotics Undergrad Sem
ROBOT 5960 - 001 Adv Design in Robotics
Prereq - Robotics I: Mechanics. This course will introduce advanced mechanism design techniques used in robotics, including, but are not limited to, the following topics: 1) parallel mechanisms, 2) cable-driven systems, 3) series elastic actuation, 4) gravity balancing, and 5) underactuated systems. The purpose of this course is to broadly introduce these advanced design methods so that senior undergraduate students and robotics graduate students, regardless of research backgrounds, can be made aware of these techniques and integrate them with their own research.
ROBOT 5960 - 001 Adv Design in Robotics
- Class Number: 12860
- Instructor: ZHANG, HAOHAN
- Component: Special Topics
- Type: In Person
- Units: 3.0
- Requisites: Yes
- Wait List: Yes
- Seats Available: 5
Prereq - Robotics I: Mechanics. This course will introduce advanced mechanism design techniques used in robotics, including, but are not limited to, the following topics: 1) parallel mechanisms, 2) cable-driven systems, 3) series elastic actuation, 4) gravity balancing, and 5) underactuated systems. The purpose of this course is to broadly introduce these advanced design methods so that senior undergraduate students and robotics graduate students, regardless of research backgrounds, can be made aware of these techniques and integrate them with their own research.
ROBOT 5960 - 002 Advanced AI
Pre-reqs: CS 3960 OR CS 4300 OR CS 5350 OR CS 5353. This course focuses on advanced algorithms for intelligent sequential decision making with a focus on deep learning-based methods and human-AI interaction. The class will cover both the theory and practical details of the algorithms behind recent breakthroughs in many types of AI decision making, including game playing, robotics, recommendation systems, and large language models. Topics include bandit algorithms, Markov decision processes, reinforcement learning, imitation learning, inverse reinforcement learning, reinforcement learning from human feedback, and AI safety and alignment.
ROBOT 5960 - 002 Advanced AI
- Class Number: 17461
- Instructor: BROWN, DANIEL
- Component: Special Topics
- Type: In Person
- Units: 3.0
- Requisites: Yes
- Wait List: Yes
- Seats Available: 5
Pre-reqs: CS 3960 OR CS 4300 OR CS 5350 OR CS 5353. This course focuses on advanced algorithms for intelligent sequential decision making with a focus on deep learning-based methods and human-AI interaction. The class will cover both the theory and practical details of the algorithms behind recent breakthroughs in many types of AI decision making, including game playing, robotics, recommendation systems, and large language models. Topics include bandit algorithms, Markov decision processes, reinforcement learning, imitation learning, inverse reinforcement learning, reinforcement learning from human feedback, and AI safety and alignment.
ROBOT 5960 - 003 Introduction to Physical HRI
Creating effective robotic systems that physically interface with their human users — “physical human–robot interaction (HRI)” systems — requires a holistic understanding of many complex, interrelated design decisions: how information should be collected from the human user; how such information should be safely and effectively mapped to robot control signals; what robot morphologies are best suited to the desired interaction; how the robot should provide feedback to the human user; and how both user and robot can learn and adapt to improve interaction quality. This course will explore these questions and associated system components both separately and in combination through lectures on foundational topics, readings and presentations on key literature, in-depth discussions, and an extensive final project. The first half of the course will provide an overview of design approaches for each component of a physical HRI system (the human model, the human sensing interface, the robot model, the control mapping, the feedback system, and the learning/adaptation framework); the second half will survey integrated physical HRI systems in various domains (assistance, rehabilitation, surgery, and teleoperation).
ROBOT 5960 - 003 Introduction to Physical HRI
- Class Number: 17516
- Instructor: Hallock, Laura
- Component: Special Topics
- Type: In Person
- Units: 3.0
- Requisites: Yes
- Wait List: Yes
- Seats Available: 5
Creating effective robotic systems that physically interface with their human users — “physical human–robot interaction (HRI)” systems — requires a holistic understanding of many complex, interrelated design decisions: how information should be collected from the human user; how such information should be safely and effectively mapped to robot control signals; what robot morphologies are best suited to the desired interaction; how the robot should provide feedback to the human user; and how both user and robot can learn and adapt to improve interaction quality. This course will explore these questions and associated system components both separately and in combination through lectures on foundational topics, readings and presentations on key literature, in-depth discussions, and an extensive final project. The first half of the course will provide an overview of design approaches for each component of a physical HRI system (the human model, the human sensing interface, the robot model, the control mapping, the feedback system, and the learning/adaptation framework); the second half will survey integrated physical HRI systems in various domains (assistance, rehabilitation, surgery, and teleoperation).
ROBOT 6100 - 001 Robotics II: Control
Required pre-req knowledge in concepts from: (ROBOT 6000/ ME EN 6220/ ECE 6650/ CS 6310) AND (ME EN 6200/ ECE 6615)
ROBOT 6100 - 001 Robotics II: Control
- Class Number: 11140
- Instructor: MASCARO, STEPHEN
- Component: Lecture
- Type: In Person
- Units: 3.0
- Requisites: Yes
- Wait List: Yes
- Fees: $40.00
- Seats Available: 6
Required pre-req knowledge in concepts from: (ROBOT 6000/ ME EN 6220/ ECE 6650/ CS 6310) AND (ME EN 6200/ ECE 6615)
ROBOT 6200 - 001 Motion Planning
ROBOT 6200 - 001 Motion Planning
- Class Number: 11111
- Instructor: Hermans, Tucker
- Component: Lecture
- Type: In Person
- Units: 3.0
- Requisites: Yes
- Wait List: No
- Seats Available: 12
ROBOT 6500 - 001 Advanced Mechatronics
ROBOT 6500 - 001 Advanced Mechatronics
- Class Number: 13115
- Instructor: LENZI, TOMMASO
- Component: Lecture
- Type: In Person
- Units: 4.0
- Requisites: Yes
- Wait List: No
- Fees: $95.00
- Seats Available: 0
ROBOT 6800 - 001 Robotics Grad Seminar
ROBOT 6800 - 001 Robotics Grad Seminar
ROBOT 6960 - 001 Adv Design in Robotics
Prereq - Robotics I: Mechanics This course will introduce advanced mechanism design techniques used in robotics, including, but are not limited to, the following topics: 1) parallel mechanisms, 2) cable-driven systems, 3) series elastic actuation, 4) gravity balancing, and 5) underactuated systems. The purpose of this course is to broadly introduce these advanced design methods so that senior undergraduate students and robotics graduate students, regardless of research backgrounds, can be made aware of these techniques and integrate them with their own research.
ROBOT 6960 - 001 Adv Design in Robotics
- Class Number: 13224
- Instructor: ZHANG, HAOHAN
- Component: Special Topics
- Type: In Person
- Units: 3.0
- Requisites: Yes
- Wait List: No
- Seats Available: 8
Prereq - Robotics I: Mechanics This course will introduce advanced mechanism design techniques used in robotics, including, but are not limited to, the following topics: 1) parallel mechanisms, 2) cable-driven systems, 3) series elastic actuation, 4) gravity balancing, and 5) underactuated systems. The purpose of this course is to broadly introduce these advanced design methods so that senior undergraduate students and robotics graduate students, regardless of research backgrounds, can be made aware of these techniques and integrate them with their own research.
ROBOT 6960 - 002 Advanced AI
Pre-req knowledge: Students should have taken a previous AI or ML class. This course focuses on advanced algorithms for intelligent sequential decision making with a focus on deep learning-based methods and human-AI interaction. The class will cover both the theory and practical details of the algorithms behind recent breakthroughs in many types of AI decision making, including game playing, robotics, recommendation systems, and large language models. Topics include bandit algorithms, Markov decision processes, reinforcement learning, imitation learning, inverse reinforcement learning, reinforcement learning from human feedback, and AI safety and alignment.
ROBOT 6960 - 002 Advanced AI
- Class Number: 12861
- Instructor: BROWN, DANIEL
- Component: Special Topics
- Type: In Person
- Units: 3.0
- Requisites: Yes
- Wait List: Yes
- Seats Available: 3
Pre-req knowledge: Students should have taken a previous AI or ML class. This course focuses on advanced algorithms for intelligent sequential decision making with a focus on deep learning-based methods and human-AI interaction. The class will cover both the theory and practical details of the algorithms behind recent breakthroughs in many types of AI decision making, including game playing, robotics, recommendation systems, and large language models. Topics include bandit algorithms, Markov decision processes, reinforcement learning, imitation learning, inverse reinforcement learning, reinforcement learning from human feedback, and AI safety and alignment.
ROBOT 6960 - 003 Introduction to Physical HRI
Creating effective robotic systems that physically interface with their human users — “physical human–robot interaction (HRI)” systems — requires a holistic understanding of many complex, interrelated design decisions: how information should be collected from the human user; how such information should be safely and effectively mapped to robot control signals; what robot morphologies are best suited to the desired interaction; how the robot should provide feedback to the human user; and how both user and robot can learn and adapt to improve interaction quality. This course will explore these questions and associated system components both separately and in combination through lectures on foundational topics, readings and presentations on key literature, in-depth discussions, and an extensive final project. The first half of the course will provide an overview of design approaches for each component of a physical HRI system (the human model, the human sensing interface, the robot model, the control mapping, the feedback system, and the learning/adaptation framework); the second half will survey integrated physical HRI systems in various domains (assistance, rehabilitation, surgery, and teleoperation).
ROBOT 6960 - 003 Introduction to Physical HRI
- Class Number: 11328
- Instructor: Hallock, Laura
- Component: Special Topics
- Type: In Person
- Units: 3.0
- Requisites: Yes
- Wait List: Yes
- Seats Available: 1
Creating effective robotic systems that physically interface with their human users — “physical human–robot interaction (HRI)” systems — requires a holistic understanding of many complex, interrelated design decisions: how information should be collected from the human user; how such information should be safely and effectively mapped to robot control signals; what robot morphologies are best suited to the desired interaction; how the robot should provide feedback to the human user; and how both user and robot can learn and adapt to improve interaction quality. This course will explore these questions and associated system components both separately and in combination through lectures on foundational topics, readings and presentations on key literature, in-depth discussions, and an extensive final project. The first half of the course will provide an overview of design approaches for each component of a physical HRI system (the human model, the human sensing interface, the robot model, the control mapping, the feedback system, and the learning/adaptation framework); the second half will survey integrated physical HRI systems in various domains (assistance, rehabilitation, surgery, and teleoperation).
ROBOT 6970 - 001 Master's Thesis
Students should enroll in the section corresponding to their Robotics Master's Thesis Advisor. Please contact the Robotics Graduate Advisor for registration information.
ROBOT 6970 - 001 Master's Thesis
- Class Number: 12914
- Instructor: MASCARO, STEPHEN
- Component: Thesis Research
- Type: In Person
- Units: 1.0 - 12.0
- Wait List: No
- Seats Available: 8
Students should enroll in the section corresponding to their Robotics Master's Thesis Advisor. Please contact the Robotics Graduate Advisor for registration information.
ROBOT 7970 - 001 PhD Dissertation
Students should enroll in the section corresponding to their Robotics PhD Dissertation Advisor. Please contact the Robotics Graduate Advisor for registration information.
ROBOT 7970 - 001 PhD Dissertation
- Class Number: 12888
- Instructor: MASCARO, STEPHEN
- Component: Thesis Research
- Type: In Person
- Units: 1.0 - 12.0
- Requisites: Yes
- Wait List: No
- Seats Available: 8
Students should enroll in the section corresponding to their Robotics PhD Dissertation Advisor. Please contact the Robotics Graduate Advisor for registration information.