Open Thesis

Real-Time 3D Object Tracking and Pose Estimation of Textureless Objects

Stichworte:
computer vision, machine learning, digital twin

Beschreibung

Real time 3D tracking of objects using one or more cameras is crucial to build a Digital Twin. In this project, you will improve an algorithm for 3D tracking and pose estimation, and use it to update a Digital Twin of a factory environment that is used in robotic manipulation tasks.

We will pay special attention to the tracking of textureless objects and the speed of the algorithm. We will also try to compare the results using one and more cameras.

Voraussetzungen

For this work, good knowledge of C++ is required.

Some knowledge of Python and ROS will be useful, but it is not required.

Kontakt

diego.prado@tum.de

Betreuer:

Diego Fernandez Prado

Force Rendering for Model Mediated Teleoperation

Stichworte:
Haptics, Force Rendering, Digital Twin, Sensors, Robotics

Beschreibung

A Digital Twin is a virtual representation of an asset, to which is connected in a bi-directional way: changes happening in the real asset are shown in the digital asset and vice-versa.

In this project, you will improve force rendering algorithms to make teleoperation more user friendly through means of the Digital Twin of a factory. 

Voraussetzungen

Required:

  • Python knowledge
  • Chai3D (ideally you have participated in the Computational Haptics Laboratory)

Recommended (not all of them):

  • Experience in ROS
  • C++ knowledge
  • Robotics knowledge
  • MuJoCo

Kontakt

diego.prado@tum.de

Betreuer:

Diego Fernandez Prado

Reinforcement Learning for Estimating Virtual Fixture Geometry to Improve Robotic Manipulation

Beschreibung

Robotic teleoperation is often used to accomplish complex tasks remotely with human-in-the-loop. In cases, where the task requires very precise manipulation, virtual fixtures can be used to restrict and guide the motion of the end effector of the robot while the person teleoperates. In this thesis, we will analyze the geometry of virtual fixtures depending on the scene and task. We will use reinforcement learning to estimate ideal virtual fixture model parameters. At the end of the thesis, the performance can be evaluated with user experiments.

Voraussetzungen

Useful background:

- Machine learning (Reinforcement Learning)

- Robotic simulation

 

Requirements:

- Experience with Python & Deep learning frameworks (PyTorch / Tensorflow...)

- Experience with a RL framework

- Motivation to yield a good outcome

 

Kontakt

(Please provide your CV and transcript in your application)

 

furkan.kaynar@tum.de

diego.prado@tum.de

 

Betreuer:

Diego Fernandez Prado

Ongoing Thesis

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