3D object model extraction during teleoperation
Beschreibung
Robotic teleoperation is applied often for tasks that the robots are not proficient at. During teleoperation, the operator observes the remote scene via a camera. On the other hand, most robots additionally have a depth sensor which can be used to extract useful information for future tasks.
In this project we will analyze and test the situations, where we can use the recorded depth data to extract and reconstruct the 3D model of a novel object grasped and manipulated by the robot.
Voraussetzungen
Necessary and useful backgrond:
- ROS, python, C++
- Image processing, video processing
Additionally:
- Motivation to yield a good work
Kontakt
furkan.kaynar@tum.de
(Please provide your CV and transcript in your application)
Betreuer:
Implementation of robotic motion planning
Beschreibung
The motion planner of a robotic arm requires planning of the necessary motion, under collision avoidance and regarding the joint limitations of the robot. In this project, we will focus on motion planning of the Panda robot arm, using several planners. We will test OpenRave motion planner and compare it to the moveit motion planner.
We will also implement and test cartesian path planning using methods like the Descartes path planner.
At the end of this project, the student will learn about implementation and usage of different motion/path planners.
Voraussetzungen
Useful background:
- Robotic control
- Experience with ROS
Necessary background:
- Experience with C++
Kontakt
furkan.kaynar@tum.de
(Please provide your CV and transcript in your application)
Betreuer:
High-level Robotic Teleoperation via Scene Editing
Beschreibung
Autonomous grasping and manipulation are complicated tasks which require precise planning and a high level of scene understanding. Although robot autonomy is evolving since decades, there is still need for improvement, especially for operating in unstructured environments like households. Human demonstration can improve the autonomous robot abilities further to increase the task success in different scenarios. In this thesis we will work on user interaction methods for describing a robotic task via modifying the viewed scene.
Voraussetzungen
Useful background:
- 3D Human-computer interfaces
- Game Design
- Digital signal processing
Required abilities:
- Experience with Unity and ROS
- Motivation to yield a good work
Kontakt
furkan.kaynar@tum.de
(Please provide your CV and transcript in your application)
Betreuer:
Robotic grasp learning from human demonstrations
Beschreibung
Autonomous grasping and manipulation are complicated tasks which require precise planning and a high level of scene understanding. Although robot autonomy is evolving since decades, there is still need for improvement, especially for operating in unstructured environments like households. Human demonstration can improve the autonomous robot abilities further to increase the task success in different scenarios. In this thesis we will work on learning from human demonstration for improving the robot autonomy.
Voraussetzungen
Required background:
- Digital signal processing
- Computer vision
- Neural networks and other ML algorithms
Required abilities:
- Experience with Python or C++
- Experience with Tensorflow or PyTorch
- Motivation to yield a good thesis
Kontakt
furkan.kaynar@tum.de
(Please provide your CV and transcript in your application)
Betreuer:
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:
Robotic task learning from human demonstration
Beschreibung
Autonomous grasping and manipulation are complicated tasks which require precise planning and a high level of scene understanding. Although robot autonomy is evolving since decades, there is still need for improvement, especially for operating in unstructured environments like households. Human demonstration can improve the autonomous robot abilities further to increase the task success in different scenarios. In this thesis we will work on learning from human demonstration for improving the robot autonomy.
Voraussetzungen
Required background:
- Digital signal processing
- Computer vision
- Neural networks and other ML algorithms
Required abilities:
- Experience with Python or C++
- Experience with Tensorflow or PyTorch
- Motivation to yield a good thesis
Kontakt
furkan.kaynar@tum.de
(Please provide your CV and transcript in your application)