Sonya Yang is a Software Development Engineer under AMD Research and Advanced Development (RAD). She oversees academic and research collaborations across the Asia-Pacific regions and focuses on AI models and sim-to-real research. Her work focuses on building simulation training workflows that enable robots to perform tasks more efficiently and safely, leveraging advanced algorithms to bridge the gap between virtual and real-world performance.

This talk offers an overview of AMD’s AI ecosystem, highlighting both software and hardware advancements. Key topics include ROCm 7, AMD’s latest open-source AI platform, and the Ryzen AI processors, which utilize NPU and iGPU acceleration for efficient on-device AI inference. We will delve into ROCm 7's newest features and discuss AMD's core focus areas in artificial intelligence. Additionally, the talk demonstrates the practicality of deploying robotics applications on AMD AI PCs. Participants will learn how to harness NPU computing power for essential computer vision tasks like face detection and depth estimation, and how to utilize open-source physical simulation tools within the AMD software environment. The session will also include demonstrations showcasing how to connect an AMD AI PC to a robotic arm, allowing attendees to experience firsthand the intuitive operation of robotic systems.

Wei-Fang Sun is currently a Solution Architect at NVIDIA AI Technology Center (NVAITC), where his main research areas include Reinforcement Learning, Generative Modeling, and Robotics applications and research.

The next wave of AI breakthroughs is expected to emerge from the widespread adoption of AI-powered robotics. By transforming industries such as manufacturing, healthcare, and transportation, intelligent robots are set to catalyze innovation and streamline operations across numerous sectors. This introductory course provides a concise overview of NVIDIA's robotics platform, designed to accelerate robotics development in this rapidly evolving field. Attendees will learn about leveraging NVIDIA Omniverse alongside OpenUSD to create high-fidelity digital twins for synthetic data generation and factory optimization. They will also be introduced to Isaac Sim, an advanced simulation tool that enables developers to build, test, and validate robotic applications in realistic, physics-accurate virtual environments. The session will also explore Isaac Lab, highlighting its role in training AI models through reinforcement learning (RL) and imitation learning (IL), enabling robots to perform complex tasks that traditional controllers struggle with. In addition, attendees will learn how to deploy robust robotic solutions using Isaac ROS, ROS2, and NVIDIA Jetson AGX, the world's most powerful embedded AI platform for robotics. By the end of the session, attendees will have a high-level understanding of how NVIDIA's ecosystem supports robotics innovation, and how they can leverage these software and hardware tools for their own future projects.

Shao-Hua Sun is an Assistant Professor in the Department of Electrical Engineering at National Taiwan University (NTU). He received his Ph.D. from the University of Southern California and his B.S. from NTU. His research spans machine learning, robot learning, reinforcement learning, and program synthesis. His work has been published at leading venues, including NeurIPS, ICML, CoRL, ICLR, etc. He has organized tutorials and workshops at NeurIPS, ICML, RLC, and CoRL.

Recent advances in artificial intelligence and machine learning have dramatically enhanced machines’ abilities to perceive visual scenes, understand natural language, and even surpass human experts in complex games. Yet, enabling robots to operate robustly in unstructured environments, manipulate novel objects, and acquire new skills—ultimately to relieve humans from tedious or hazardous tasks—remains a formidable challenge. My research aims to build a program-guided robot learning framework that is both interpretable and generalizable, allowing robots to acquire long-horizon, hierarchically structured skills such as furniture assembly and cooking. In this framework, desired behaviors are represented as executable programs, while primitive skills are learned as reusable modules for carrying out higher-level plans. This talk will present a series of projects that progressively develop and integrate these ideas, advancing toward a unified framework for interpretable, generalizable, and skillful robot learning.

Chung-Yi Weng is currently a Research Scientist at Google fascinated by AI, 3D, and their interaction. Before joining Google, he received his PhD degree from the University of Washington in 2023, where he primarily worked on rendering dynamic humans in 3D from unstructured data. He previously interned at Meta and Google VR/AR, and closely collaborated with Google Research. Beyond his passion for computer science, he is also a die-hard tennis fan and has been very proud of successfully combining his research with his enthusiasm for this sport. Additionally, he is the recipient of the ACM Multimedia Best Paper Award, Microsoft Endowed Fellowship, and UW Reality Lab Huawei and Google Fellowship.

In this talk, I would like to share the lessons I learned during my PhD journey on identifying interesting research problems and tackling difficult challenges. My PhD research focus is on reconstructing and rendering high-quality dynamic humans using unstructured data from the wild, such as internet photos or YouTube videos. This task is particularly challenging due to 3D-to-2D ambiguity, highly dynamic motion, appearance variations, and the inherently non-rigid deformation nature. I will present several of my works that address these challenges and explain how they come together to form a central theme of my PhD career. Finally, I will share my suggestions for students looking to apply to CS PhD programs at US universities, highlighting the key factors that need to be considered before making this crucial decision.

王聖智教授於 1984 年畢業於國立交通大學電子工程學系，之後分別於 1990 年及 1994 年獲得美國史丹佛大學 (Stanford University) 電機工程系的碩士學位及博士學位。研究所博士班畢業後，繼續於史丹佛大學資訊工程系擔任一年的助理研究員 (Research Associate)，然後返國任職於國立交通大學電子研究所迄今。他的研究領域主要包括影像處理、電腦視覺、機器學習等領域。

監督式學習技術 (Supervised Learning) 與自監督式學習技術 (Self-Supervised Learning) 都是近年來在 AI 領域中的重要技術。在這次的演講中，我們將根據時間上的發展次序，一路從監督式學習技術的發展介紹到自監督式學習技術的興起，讓學員們可以認識及比較一下這兩項 AI 領域中的重要技術。

Wei-Fang Sun is currently a Solution Architect at NVIDIA AI Technology Center (NVAITC), where his main research areas include Reinforcement Learning, Generative Modeling, and Robotics applications and research.

The next wave of AI breakthroughs is expected to emerge from the widespread adoption of AI-powered robotics. By transforming industries such as manufacturing, healthcare, and transportation, intelligent robots are set to catalyze innovation and streamline operations across numerous sectors.

This introductory course provides a concise overview of NVIDIA's robotics platform, designed to accelerate robotics development in this rapidly evolving field. Attendees will learn about leveraging NVIDIA Omniverse alongside OpenUSD to create high-fidelity digital twins for synthetic data generation and factory optimization. They will also be introduced to Isaac Sim, an advanced simulation tool that enables developers to build, test, and validate robotic applications in realistic, physics-accurate virtual environments. The session will also explore Isaac Lab, highlighting its role in training AI models through reinforcement learning (RL) and imitation learning (IL), enabling robots to perform complex tasks that traditional controllers struggle with. In addition, attendees will learn how to deploy robust robotic solutions using Isaac ROS, ROS2, and NVIDIA Jetson AGX, the world's most powerful embedded AI platform for robotics. By the end of the session, attendees will have a high-level understanding of how NVIDIA's ecosystem supports robotics innovation, and how they can leverage these software and hardware tools for their own future projects.

俞冠廷博士於 2018 年自麻省理工學院 (MIT) 電機工程與計算機科學系 (EECS) 取得博士學位，隨後創立 XYZ Robotics。他的研究興趣為機器人操作，特別是透過物理接觸進行狀態估測，以追蹤操作過程中物體的狀態變化。他曾在 2016 年的 IROS 國際會議中獲得最佳論文提名，並積極投身於機器人系統的實際應用。他曾參與 2013 年的 DARPA 機器人挑戰賽，致力於解決災難救援任務。此外，作為 2015 年至 2017 年間亞馬遜機器人挑戰賽的系統負責人，他在 2017 年贏得了物品存放任務的第一名。

倉庫和工廠的人力短缺加速了自動化解決方案的推廣。電腦視覺結合機器人技術，成為實現自動化任務多樣性的重要關鍵技術。在此次演講中，我將介紹 XYZ 公司在機器人抓取技術上的發展歷程——從大學實驗室的研究到為全球數千位客戶提供成功解決方案。我會分享從研究到工業應用之間的差距，與電腦視覺相關的挑戰，以及給希望從事機器人相關工作者的職業建議。

Chieh-Chih (Bob) Wang earned his Ph.D. in Robotics from the School of Computer Science at Carnegie Mellon University. From 2004 to 2005, he served as an Australian Research Council (ARC) Research Fellow of the ARC Centre of Excellence for Autonomous Systems and the Australian Centre for Field Robotics at the University of Sydney. Between 2005 and 2015, he was a faculty member in the Department of Computer Science and Information Engineering and the Graduate Institute of Networking and Multimedia at National Taiwan University. From 2015 to 2016, Dr. Wang worked at the Special Projects Group of Apple Inc. He is currently a professor with the College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University and serves as the Chief Digital Officer at the Mechanical and Mechatronics Systems Research Laboratories (MMSL) at the Industrial Technology Research Institute (ITRI).

Since 1993, I have worked on robotics projects spanning a wide range of applications, including autonomous underwater vehicles at NTU, Mars Rovers at NASA Ames Research Center, self-driving cars at CMU, humanoids for RoboCup and Intelligent Transportation Systems at NTU, and self-driving cars again at Apple, NYCU and ITRI. In this talk, I will share the lessons learned from these field studies and discuss the key challenges of deploying robots in real-world environments.