Developing feedback controllers for robots with embedded sensors is challenging and typically requires expert knowledge. As machine learning (ML) advances, the development of learning-based controllers has become more and more accessible, even to non-experts. This work presents the development of a tutorial to educate non-roboticists about ML-based sensing and control in cyber-physical systems using a soft robotic device. We demonstrated this by creating a recurrent neural network-based closed-loop force controller for a soft finger with embedded soft sensors. Our hypothesis is validated in a 2.5-hour workshop session for students with no prior knowledge of robot control. This work serves as a tutorial for participants aiming to experience and perform a general benchmark for soft robot control tasks, with little or even no expertise in robotics.
Machine Learning for Soft Robot Sensing and Control: A Tutorial Study
2022-07-18
In: Proceedings of the 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS). IEEE: Coventry, UK. (2022)
Paper
Electronic Resource
English
DDC: | 629 |
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