Fundamental ideas and techniques for constructing intelligent (robotic) systems acting in the world. Perception (including approaches based on deep learning and approaches based on 3D geometry), planning (robot kinematics and trajectory generation, collision-free motion planning, task-and-motion planning, and planning under uncertainty), as well as dynamics and control (both model-based and learning-based). Deep reinforcement learning approaches (policy gradients, actor-critic algorithms, value function and q-function methods, inverse reinforcement learning, meta-learning). Highlights of state-of-the-art methods and application domains.
İlk dosyayı sen ekleyebilirsin — notlar, geçmiş finaller, çözümler, cheat-sheet, ne varsa. Drive linki / PDF / ZIP / fotoğraf, hepsi olur.
Şu an: mail at, ben düzenleyip yayına alayım. Form/upload UX yakında geliyor (Kimya tasarlıyor).
There is no final exam for this course, however, any one of the following will directly result in an F grade: (1) not submitting a project or homework (including report), (2) being absent in the midterm, (3) being absent in a project presentation.
We follow the Generative AI policy guideline of Bilkent University which can be found here: https://w3.bilkent.edu.tr/bilkent/generative-artificial-intelligence-genai-guideline/
Intelligent interaction: Passive vs. Active/Embodied AI Robotics Fundamentals: Kinematics (FK, IK, Diff IK, Optimization) Kinematics + Motion Planning Motion Planning (Trajectory Optimization, Sampling-based Methods) Perception: geometric vision, point clouds + integration with robotic tasks Deep perception: representation learning for robotic manipulation Vision-Language-Action (VLAs) Models Vision-Language-Action (VLAs) Models Vision-Language-Action (VLAs) Models Deep Reinforcement Learning (DRL) Deep Reinforcement Learning (DRL) Sequential Robotic Manipulation, Task and Motion Planning (TAMP) Learning for Task and Motion Planning (TAMP) Final project presentations ECTS - Workload Table: Activities Number Hours Workload Homework 6 8 48 Course hours 14 3 42 Midterm exam 1 2 2 Preparation for Midterm exam 1 16 16 Project (including preparation and presentation if applicable) 1 45 45 Total Workload: 153 Total Workload / 30: 153 / 30 5.1 ECTS Credits of the Course: 5 Type of Course: Lecture - Independent Study - Project Course Material: Lecture Notes - Slides Teaching Methods: Assignment - Presentations