Learning & Understanding Human-Centered Robotic Manipulation Strategies
Human-robot collaboration is one of the most promising applications of autonomous robots. For this, robots need to interact with humans in a meaningful, flexible and adaptable manner, especially when new situations are faced. Currently, the new emerging technologies such as virtual reality and wearable devices allow capturing natural human movements of multiple users. Then, the next generation of learning methods should take advantage of and bootstrap the learning of new activities by adapting to the massive processing of information obtained from these enhanced sensors.
The goal of this PhD project is to develop semantic-based learning algorithms able to cope with a large amount of data generated from multi-modal/multi-level sensors and react to dynamically changing environments in real-time to produce robots with enhanced autonomy levels and manipulation capabilities. The massive collection of data will also contain information about the different styles of human demonstrations which will help in developing a more human-centred control solution.
Karinne Ramirez-Amaro (contact)
Assistant Professor at Chalmers, Electrical Engineering, Systems and control, Mechatronics
Doctoral Student at Chalmers, Electrical Engineering, Systems and control, Mechatronics
Researcher at Chalmers, Electrical Engineering, Systems and control, Mechatronics
Full Professor at Chalmers, Electrical Engineering, Systems and control, Mechatronics
Chalmers AI Research Centre
Chalmers AI Research Centre (CHAIR)
Funding Chalmers participation during 2020–2025
Related Areas of Advance and Infrastructure
Information and Communication Technology
Areas of Advance