CKSRI Seminar Series 2026 “Learning Generalizable Robotic Assembly Policies for Complex Objects"

11:00am - 12:00pm
LTG, HKUST

Supporting the below United Nations Sustainable Development Goals:支持以下聯合國可持續發展目標:支持以下联合国可持续发展目标:

ABSTRACT:

Assembly is a cornerstone of modern manufacturing and construction, enabling the creation of complex, large-scale products from modular components. Despite its importance, assembly remains a labor-intensive process often requiring hundreds of intricate steps. While robots are increasingly used to automate these tasks, most current systems rely on rigid, pre-programmed procedures that struggle with the "long-horizon" nature of complex assembly. In this talk, we explore the potential of leveraging Artificial Intelligence to revolutionize robotic planning. Drawing inspiration from the success of large-scale pretraining and reinforcement learning (RL) fine-tuning in LLMs, we propose a framework for building robotic assembly policies that are safe, efficient, and highly adaptive to new objects and environments.

講者/ 表演者:
Prof. Ziqi Wang

Prof. Ziqi Wang is an Assistant Professor at the Division of Integrative Systems and Design (ISD) at HKUST. Before joining HKUST, he worked as a postdoctoral researcher both at EPFL (2024) and ETH Zurich (2022-2024). He obtained his Ph.D. in Computer Science at EPFL in 2021. He completed his bachelor's degree in Mathematics in 2017 at the University of Science and Technology of China (USTC).

語言
英文
適合對象
校友
長者
教職員
公眾
科大家庭
研究生
本科生
主辦單位
HKUST Robotics Institute
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