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Multifingered Robot Hand Simulation and Control

Human-Robot Interaction

Robot Control

The objective of this 6-month master's thesis is to develop a novel framework for calibrating joint torque sensors of serial manipulators. The (re-)calibration of joint side torque sensors is crucial for safety, particularly as robots and humans increasingly operate in shared environments. Recalibrating an assembled robotic manipulator requires precise knowledge of its kinematic and dynamic model, a task already addressed in previous research regarding inertial parameter identification of serial manipulators. The final goal is to introduce an "off-calibration" score indicating the current calibration status of the arm and the need for recalibration. Additionally, if recalibration is necessary, the novel recalibration framework can be utilized to update sensor parameters.

The main task would be:

  • Understanding the existing inertial identification framework;
  • Literature research on calibration frameworks for robotic manipulators;
  • Developing and prototyping the novel framework in simulation;
  • Come up with scenarios to test the overall system;
  • Implementation of a real robotic system.

Pre-requisites:

  • Experience with prototyping in Matlab, Python and C++;
  • Basics of Manipulator Kinematics and Dynamics;
  • Basic working knowledge of Manipulator Control;

Helpful but not required:

  • Knowledge of inertial parameter identification, sensor calibration and running simple controllers on robots

 

For more information, please contact:

Mario Tröbinger (mario.troebinger@tum.de)

 

Telepresence

Human modeling

Brain Computer Interface Systems

The potential for invasive Brain Computer Interface (iBCI) systems to dramatically improve the livelihoods of those who have lost motor functionality has increased in recent years. This improvement has emerged due in part to advances in the field of deep learning, but the performance of these models varies significantly depending on the quality of the data collection and the design of the deep learning model architectures. This project focuses on designing and refining deep learning architectures tailored for decoding spiking neural data, with an emphasis on handling sparse and binary input data, continuous output kinematic features, and maintaining a small footprint to facilitate real-time inference. Research with this project will provide experience in pushing the boundaries of what's possible with iBCI systems and will involve working in a collaborate environment for one of the most difficult decoding tasks in the bio-engineering field. Researchers will have access to state-of-the-art facilities and computational resources and will benefit from mentorship opportunities and professional development.
 

Tasks may include a few of the below (to be discussed depending on your interest and background)

  • Further develop and optimize an existing decoding pipeline in python
  • Develop state-of-the-art deep learning models based on convolutional and/or  recurrent neural networks
  • Investigate intermediate latent spaces that may exist between the neural data and the associated kinematic features
  • Create a reporting mechanism to make assessment of different architectures efficient, which will enhance our ability to rapidly iterate and improve decoding models

Prerequisites

  • Proven experience with building deep learning architectures in Tensorflow
  • Proficiency in python 

Helpful but not required

  • Experience with real-time inference with trained models
  • Theoretical understanding of manifolds and dimensionality reduction methods

Position

Current project is targeted for a research internship or semester thesis. It’s not a paid position, nor a master thesis ad.

Related Literature

 

For more information, please contact:

Dr. Alexander Craik (alex.craik@tum.de)

Ioannis Xygonakis (ioannis.xygonakis@tum.de)

Robot Learning

Mechatronics System Developement

Student Assistants (HiWi)

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