Imitation learning or Learning from Demonstration (LfD) is a framework that enables fast and efficient teaching of skills to robots by providing them with the examples of the required tasks. Behaviour cloning (BC) methods (such as Gaussian Mixture Modesl (GMM), Hidden Markov Models (HMM), Dynamic Movement Primitives (DMP) have already shown success in trajectory representation and learning. Not only the positional trajectories, but also the interaction forces play an important role in the task success. In this work, we would like to study the impact of force generalization and hybrid position-force control in the task reproduction for LfD.
Literature research on learning from demonstration for in-contact tasks
Design and implementation of in-contact tasks in CHAI3D simulation (such as push button, surface cleaning, sliding etc.)
Collection of demonstrations via haptic interface
Development of the learning algorithm
Evaluation and comparison of position+force and position only learning
F. Steinmetz, A. Montebelli, and V. Kyrki, "Simultaneous kinesthetic teaching of positional and force requirements for sequential in-contact tasks", 2015 IEEE-RAS 15th International Conference on Humanoid Robotics.
 M. Racca, J. Pajarinen, A. Montebelli and V. Kyrki, "Learning in-contact control strategies from demonstration," 2016 IEEE/RJS International Conference on Intelligent Robots and Systems (IROS)
 C. Zeng, C.Yang, J.Zhong, and J.Zhang, "Encoding Multiple Sensor Data for Robotic Learning Skills from Multimodal Demonstration", in IEEE Acces, vol. 7, pp. 145604-145613, 2019.
Keywords: Haptics, Vibrotactile, Signal Processing, Source Separation, Denoising
Microscoping roughness sensations can be captured with the vibrations induced by sliding motion over a textured surface. However, the captured signals can be affected by various factors, such as other vibrations of external systems or sensor inaccuracies. An important goal is to be able to filter signals and remove noise components and imperfections. To do this, source separation is a promising approach. The idea is to separate different components in signals originating from different sources, respectively. After separation, it is possible to remove noise components and maintain only meaningful parts of the original signal. The student shall investigate different forms of distortions in vibrotactile signals and methods to separate and remove them.
MATLAB, basics in signal processing, basics in audio processing