3. 강의목표
The goal of this course is to help students explore various modern algorithms used in intelligent robot development and gain practical experience by applying them to actual robots. The course is broadly divided into two parts. (1) Before the midterm exam, students will learn various algorithms related to robotics learning. (2) After the midterm exam, students will gain the knowledge and practical programming skills necessary for Team Project Competitions.
4. 강의선수/수강필수사항
Required prerequisite subject: Electronic Mathematics A
5. 성적평가
Attendance 10%, Midterm Exam 40%, Final Team Project 50%
6. 강의교재
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This course is primarily based on slides prepared by the instructor. Classical material covered in class can be found in .
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0000
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7. 참고문헌 및 자료
Thrun, Sebastian. "Probabilistic robotics." Communications of the ACM 45.3 (2002): 52-57.
8. 강의진도계획
Week 1 : Introduction, Basics of AI / ML / DL
Week 2 : Behavior Cloning, Inverse Reinforcement Learning
Week 3 : Robot Motion Planning
Week 4 : Filtering
Week 5 : Markov Decision Process
Week 6 : Choo-Seok
Week 7 : Kinematics
Week 8 : Mid-Term
Week 9 : Introducing Team Project Competition
Week 11 : Literatures related to Team Project Competition
Week 12 : How to use Mujoco
Week 13 : Robot Learning + Mujoco Simulator
Week 14 : PID Controller
Week 15 : Robot Learning + Real Robot
Week 16 : Team Project Competition Week
10. 학습법 소개 및 기타사항
Please bring your own laptop after the midterm exam.
The Team Project Competition will be Vision-Language-based Manipulation.
(However, the specific task titles are subject to change depending on robot availability.)
11. 장애학생에 대한 학습지원 사항
- 수강 관련: 문자 통역(청각), 교과목 보조(발달), 노트필기(전 유형) 등
- 시험 관련: 시험시간 연장(필요시 전 유형), 시험지 확대 복사(시각) 등
- 기타 추가 요청사항 발생 시 장애학생지원센터(279-2434)로 요청