When teaching robots remotely via teleoperation, the communication (between the human demonstrator and the remote robot learner) imposes challanges. Network delay, packet loss and data compression might cause completely or partially degraded demonstration qualities. In this thesis, we would like the make the best out of varying quality demostrations provided via teleoperation with haptic feedback. This will require the identification and classification of the demonstrations based on quality or reliability, and an effective integration of the demonstrations into the imitation learning algorithm.
C/C++ coding skills,
High motivation to learn and conduct research
Familiarity wih learning from demonstration ( HSMM, DMP, GMM/GMR)
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.