Speaker: Gustavo Garcia, Associate Professor, Ritsumeikan University, Japan. Organised by the CHAIR theme Interpretable AI.
Overview
- Date:Starts 21 November 2023, 13:00Ends 21 November 2023, 14:00
- Seats available:40
- Location:Edit building, room EL43, Maskingränd 2, Campus Johanneberg
- Language:English
- Last sign up date:21 November 2023
Abstract, Gustavo Garcia:
Human-robot collaboration (HRC) is essential to address workforce shortages in aging societies like in Japan and Sweden. However, current HRC lacks effective synchronization between human and robot plans, leading to inefficiencies.
Our research aims to improve communication and synchronization using mixed reality (MR) and Artificial Intelligence (AI). MR will visually convey robot plans, motions, and environmental data, while AI will interpret human input, generate robot responses, and adaptively plan the next step, with decisions conveyed via MR. Ultimately, we aim to test this framework in a collaborative assembly task, evaluating efficiency, safety, and human-centered metrics.
This talk focuses on MR-based visualization for robot motion and human safety, aiming to balance safety with efficiency.
Bio:
Gustavo Alfonso Garcia Ricardez received his MEng and PhD degrees from the Nara Institute of Science and Technology, Japan, in 2013 and 2016, respectively. He is currently an Associate Professor at the Emergent Systems Lab of Ritsumeikan University, Japan, and a Research Advisor at the Robotics Hub of Panasonic Corporation.
He leads numerous research projects and teams in international robotics competitions, achieving results such as First place in the Airbus Shopfloor Challenge (2016), Finalist in the Amazon Robotics Challenge (2017), Second place in the regional OpenCV AI competition (2021), and multiple First places in the Future Convenience Store Challenge (2018-2022). His research interests include human-safe, efficient robot control, human-robot interaction, manipulation, and task planning.
Interpretable AI
Interpretable AI is an emerging field, focused on developing AI systems that are transparent and understandable to humans.