Investigator: Harold Soh

The ability of humans to navigate our environment is heavily dependent on the quality and speed of sensing. Tactile sensing, or the sensation of touch and pressure, is especially important for both coarse and fine object manipulation tasks, such as handling parcels and writing.

In this project, we aim to develop a new prototype of tactile force sensor with integrated machine-learning capabilities. CLeAR is particularly interested in developing novel efficient machine learning methods for tactile data, and understanding how our next-generation skin can be used to enhance physical human-robot collaboration.

Funded by: National Robotics Programme (NRP)

Further Reading:

• Towards Effective Tactile Identification of Textures using a Hybrid Touch Approach, Tasbolat Taunyazov, Hui Fang Koh, Yan Wu, Caixia Cai and Harold Soh, IEEE International Conference on Robotics and Automation (ICRA), 2019
• Online Spatio-Temporal Gaussian Process Experts with Application to Tactile Classification, Harold Soh, Yanyu Su and Yiannis Demiris, IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura, Portugal, 2012. (Cognitive Robotics Best Paper Award Finalist)