Topics Advanced Seminar & Projektpraktikum

SoA on General control frameworks for service robots (mobile manipulators) and teleoperation

Advisor: Julio Rogelio Guadarrama Olvera

The classic architecture of service robots is a mobile base that carries one or two manimulators provided with a several sensors. While the potential applications is broad, the core functionality of them relies on the motion control. While the robot architectures are different from each other, the share common elements that make it feasible to develop general control frameworks that can be implemented on different platforms without the need of specific library generation. Some examples of these frameworks are the motion planner MoveIt and the ros_control framework.

References:

[1] Honerkamp, D., Welschehold, T., & Valada, A. (2021). Learning Kinematic Feasibility for Mobile Manipulation through Deep Reinforcement Learning. arXiv preprint arXiv:2101.05325.

[2] Mansard, N., Stasse, O., Evrard, P., & Kheddar, A. (2009, June). A versatile generalized inverted kinematics implementation for collaborative working humanoid robots: The stack of tasks. In 2009 International conference on advanced robotics (pp. 1-6). IEEE.

[3] Chitta, S., Sucan, I., & Cousins, S. (2012). Moveit![ros topics]. IEEE Robotics & Automation Magazine, 19(1), 18-19.

Topic: SoA on Control Strategies for Upper-Limb Exoskeletons (already taken)

Advisor: Natalia Paredes Acuña

Description: Control strategies for upper-limb exoskeletons depend on their sensory input signals such as sEMG, joint positions, force sensors, and actuation systems (motors). However, due to the erratic nature of physiological signals like sEMG, the integration of different sensor modalities, and the muscle activity and size difference among users, novel control strategies implement machine learning algorithms to estimate the user’s voluntary motion. Furthermore, control strategies should adapt to the nature of the intended therapy strategy. This means that the selected control mode depends on the user target group, e.g., stroke survivors, the actuated muscles, e.g., biceps brachii for elbow flexion, and the therapy routine that needs to be executed, e.g., sets of repetitions with partial or no active movement from the user.

References:

[1] Proietti, T., Crocher, V., Roby-Brami, A., & Jarrasse, N. (2016). Upper-Limb Robotic Exoskeletons for Neurorehabilitation: A Review on Control Strategies. IEEE reviews in biomedical engineering, 9, 4–14. doi.org/10.1109/RBME.2016.2552201

[2]   Desplenter, T., Zhou, Y., Edmonds, B. P., Lidka, M., Goldman, A., & Trejos, A. L. (2020). Rehabilitative and assistive wearable mechatronic upper-limb devices: A review. Journal of Rehabilitation and Assistive Technologies Engineering. doi.org/10.1177/2055668320917870

Causality in Artificial Cognitive Systems (already taken)

Advisor: Nicolas Berberich and Constantin Uhde

Classical supervised machine learning is focused on learning associations between variables. If two events or features are frequently observed together then observing one changes the likelihood of observing the other. For efficient action planning, a cognitive system is required to not only learn bidirectional associations but also which event causes the other event, such that it can perform the right interventions to reach its goal. This task falls under the topic of “causal inference” which gained popularity in the last years due to work by Judea Pearl, Joshua Bengio, Bernhard Schölkopf and others. The aim of this seminar project is to produce a synopsis of state-of-the-art research in causal learning for artificial cognitive systems and robotics.

References:

• David Vernon, “Artificial Cognitive Systems: A Primer”

• Judea Pearl, “Causal Inference in Statistics: A Primer”

Sensors for Prosthetic Hands to Support Sensory Feedback (already taken)

Advisor: Nicolas Berberich

Abstract: The goal of this seminar project is to critically analyze state-of-the-art research on the development of sensors for prosthetic hands. A special focus should be put on which sensor modalities are included, where the sensors are placed on the prosthetic hand and how the measured sensor values are used for sensory feedback to the prosthesis user. Methodologically, it is furthermore important to review how the sensors are scientifically evaluated, e.g. through objective performance tests or subjective user feedback.

References:

• Jaemin Kim et al. “Stretchable silicon nanoribbon electronics for skin prosthesis”, Nature Communications, 2014
• Jacob Segil et al. ”Multi-modal prosthetic fingertip sensor with proximity, contact, and force localization capabilities”, Collaborative and Controllable Robotics for Biomedical and Industrial Applications – Research, 2019
• Ahmad Alhaddad et al. “Toward 3D printed prosthetic hands that can satisfy psychosocial needs: grasping force comparisons between a prosthetic hand and human hands”, International Conference on Social Robotics, 2017