實體人工智慧

臺北科技大學 115-1 學期課程，課號 367087。

課程大綱

Physical AI refers to AI systems that can perceive, reason, and act within real-world environments—sensing through cameras, microphones, and other sensors, and interacting with the world via motors, robotic arms, wheels, or other actuators. In this course, Physical AI serves as the central lens through which all projects and discussions are contextualized. Students will explore how modern AI models are integrated into real-world systems such as robots, smart homes, and autonomous vehicles, and how intelligence, embodiment, and environment jointly shape system behavior. Intended Learning Outcomes: 1. Explain the definition, scope, and significance of Physical AI, and distinguish it from purely digital and agentic AI systems. 2. Describe the core concepts, system architectures, and key enabling technologies underlying Physical AI, including sensing, perception, reasoning, planning, and control. 3. Analyze and apply foundational Physical AI techniques to real-world problem settings, taking into account environmental dynamics, embodiment, and system constraints. 4. Identify and evaluate major application domains and representative use cases of Physical AI, such as robotics, smart environments, and autonomous systems. 5. Design, implement, and prototype a Physical AI solution that demonstrates the integrated use of perception, decision-making, and physical action.

課程進度

週次 日期 課程內容 備註 1 9/8/2026 Introduction 2 9/15/2026 Recent Advances in Physical AI 3 9/22/2026 Data Pyramid in Physical AI 4 9/29/2026 Towards Real-World Deployment of Physical AI 5 10/6/2026 Simultaneous Localization and Mapping HW1 Release 6 10/13/2026 Depth Estimation 7 10/20/2026 Path Planning and Robot Navigation 8 10/27/2026 Intelligent Driving Systems HW1 Deadline, HW2 Release 9 11/3/2026 IScenario-based Safety Evaluation Course Project Specification Release 10 11/10/2026 Introduction to Issac Sim 11 11/17/2026 Robotics Manipulation HW2 Deadline, HW3 Release 12 11/24/2026 Robot Learning for Manipulation 13 12/1/2026 Introduction ICRA'26 WBCD Challenge 14 12/8/2026 Policy Evaluation HW3 Deadline 15 12/15/2026 Human-Centered Physical AI 16 12/22/2026 Course Project Presentation Course Project Deadline

評量方式

Computer Assignment: 50% - HW1: 3D Scene Reconstruction and Mapping - HW2: Robot Navigation - HW3: Robotics Manipulation Course Project : 40% The Course Project is designed to provide students with hands-on experience across the full lifecycle of Physical AI—from real-world data collection, to policy learning, and ultimately to evaluation.

教材

【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】 使用外文原文書：是 參考書目： 1. Stuart Russell and Peter Norvig (2020). Artificial Intelligence: A Modern Approach, 4th Global ed., Pearson College. 2. Richard Hartley and Andrew Zisserman (2003), Multiple View Geometry in Computer Vision. 3. Timothy D. Barfoot (2024), State Estimation for Robotics, Second Edition, Cambridge University Press. 4. Kevin Lynch and Frank Park (2017), Modern Robotics Mechanics, Planning, and Control, Cambridge University Press. 5. Keet, C. M. (2025). An introduction toontology engineering (2nd ed.). College Publications. 6. Allemang, D. (2020). Semantic Web for the Working Ontologist : Effective Modeling for Linked Data, RDFS, and OWL / (Third Edition). Association for Computing Machinery. https://doi.org/10.1145/3382097