Learning and Adaptive Systems

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In this workshop, the speakers, a group of graduate students from Hong Kong Polytechnic University, presented their innovative research on a Mutual Cognitive Human-Robot Collaborative Manufacturing System. This system explores the concept of Industrial 5.0, focusing on human-robot collaborative work environments to improve manufacturing and assembly tasks in smart factories.

An Research and Entrepreneurship project aims to improve collaboration between humans and robots in the manufacturing industry through harnessing technologies such as cognitive Artificial Intelligence (AI) and Augmented Reality (AR). The system combines robots’ high levels of accuracy, reliability, and repeatability with humans’ flexibility and adaptability to optimise productivity for various manufacturing assembly tasks in a shared workspace, made by PolyU’s Research Group of AI for Industrial Digital Servitization (RAIDS).

Supported by Office of the Government Chief Information Officer, The Government of the Hong Kong Special Administrative Region, the Hong Kong ICT Awards (HKICTA) is a yearly flagship event in the local information and communications technology (ICT) industry. The Awards aims at recognising and promoting outstanding ICT inventions and applications, thereby encouraging innovation and excellence among Hong Kong’s ICT talents and enterprises in their constant pursuit of creative and better solutions to meet business and social needs.

CASE is the flagship conference of the IEEE Robotics & Automation Society. It provides a primary international forum for automation researchers and practitioners to present and discuss their work. CASE2022 will include plenary and keynote sessions, contributed paper sessions, workshops and tutorial sessions, industry panel discussions, exhibitions from our corporate partners, and numerous social events and student activities. The theme of the conference is AI (Artificial Intelligence) Automation.

A Research Project utilized MAML-based 5-step PPO to improve robot’s productivity in HRC by about 75% on average. It aims to enable collaborative robots (cobots) to acquire basic skills and perform tasks based on personalised production requirements, made by KWOK Hin Chi , LI Chengxi, PANG Yat Ming, ZHENG Pai.

A Research Project utilized MAML-based 5-step PPO to improve robot’s productivity in HRC by about 75% on average. It aims to enable collaborative robots (cobots) to acquire basic skills and perform tasks based on personalised production requirements, made by KWOK Hin Chi , LI Chengxi, PANG Yat Ming, ZHENG Pai.