IEEE CIS SSFML 2020

Workshops

■ Workshop 1 in Japan: Design of Locomotion Patterns and Robot Contest on Flag Strike

- Chair:
- WeiHong Chin, Tokyo Metropolitan University, Japan

Abstract

Various types of robots have been applied to educational fields. Basically, there are three different aims in robot edutainment (education with entertainment): Learning on Robots, Learning through Robots and Learning with Robots. An educational partner robot can teach something through interaction with students in daily situation. The human-robot co-learning is a kind of Learning with Robots and we have developed various types of robot partners. To enhance the natural communication and interaction with students, we have to design the robot motion. Therefore, we focus on Learning through Robots in this workshop. This workshop provides the participants with the practice on the design of locomotion patterns for multi-legged robots using ODE (Open Dynamics Engine, https://www.ode.org/) from the viewpoint of Learning through Robots. Basically, participants don’t need the programming skill, but we assumed that participants install ODE on Windows, Macintosh or UNIX PC beforehand. First, participants learn the basic mathematical formulation of robot geometry and kinematics by trigonometric functions. Next, participants understand how to conduct multi-legged locomotion by computer simulations with ODE, and design locomotion patterns by text files as a group work. Finally, participants join a flag strike robot contest.

■ Workshop 2 in Taiwan: Human and Robot Co-Learning

- Chair:
- Chang-Shing Lee, National University of Tainan, Taiwan
- Program: Please click here
- Result: Please click here

Abstract：

Dynamic assessment with an intelligent agent can differentiate the capabilities and proficiency of students. It can therefore be advocated as an interactive approach to conduct assessments on students in learning systems. We propose an AI-FML agent for robotic Go game, language, mathematics, and AIoT real-world co-learning applications. The proposed AI-FML agent publishes the inferred result to communicate with the robot Kebbi Air based on MQTT protocol to achieve the goal of human and smart machine co-learning. From Sept. 2019 to Jul. 2020, we introduced the AI-FML agent into the teaching and learning fields in Taiwan. The learning performance and feedback from students and teachers has been extremely positive, especially from remedial students. The experimental results show the robots and students can co-learn AI tools and FML applications effectively. In the future, we hope to deploy the AI-FML agent to more available robot and human co-learning platforms through the established AI-FML International Academy in the world.

Scenario and Video：

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