Offene Arbeiten

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:

Hasan Furkan Kaynar

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:

Hasan Furkan Kaynar

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:

Hasan Furkan Kaynar

Robotic grasp learning from human demonstrations

Stichworte:

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:

Hasan Furkan Kaynar

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:

Hasan Furkan Kaynar, Diego Fernandez Prado

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)

 

Betreuer:

Hasan Furkan Kaynar

Laufende Arbeiten

Masterarbeiten

Robotic task learning from human demonstration using spherical representations

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:

Hasan Furkan Kaynar

Developing of a method to solve the flexible job-shop scheduling problem with Graph Neural Networks (GNNs)

Beschreibung

...

Betreuer:

Hasan Furkan Kaynar

Depth data analysis for investigating robotic grasp estimates

Beschreibung

Many robotic applications are based on computer vision, which relies on the sensor output. A typical example is semantic scene analysis, with which the robot plans its motions. Many computer vision algorithms are trained in simulation which may or may not represent the actual sensor data realistically. Physical sensors are imperfect and the output erroneous data may deteriorate the performance of the required tasks. In this thesis, we will analyze the sensor data and estimate its effects on the final robotic task.

Voraussetzungen

Required background:

- Digital signal processing

- Image analysis / 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:

Hasan Furkan Kaynar

Forschungspraxis (Research Internships)

Interactive Segmentation with Depth Information

Beschreibung

Image segmentation is a fundamental problem in computer vision. While there are autonomous methods for segmenting a given image, interactive segmentation schemes receive a human guidance in addition to the scene information. In this project, we will investigate methods for using depth information to improve segmentation accuracy.

Voraussetzungen

Basic knowledge of

- Digital signal processing

- Computer vision

- Neural networks and other ML algorithms

 

Familiarity with

- Python or C++

- Tensorflow or PyTorch

 

 

Kontakt

furkan.kaynar@tum.de

 

(Please provide your CV and transcript in your application)

 

Betreuer:

Hasan Furkan Kaynar

3D object model reconstruction from RGB-D scenes

Beschreibung

The robots should be able to discover their environments and learn new objects in order to be a part of daily human life. There are still challenges to detect or recognize objects in unstructured environments like a household environment. For robotic grasping and manipulation, knowing 3D models of the objects are beneficial, hence the robot needs to infer the 3D shape of an object upon observation. In this project, we will investigate methods that can infer or produce 3D models of novel objects by observing RGB-D scenes. We will analyze the methods to reconstruct 3D information with different arrangements of an RGB-D camera.

 

 

 

 

 

 

 

 

 

Voraussetzungen

  • Basic knowledge of digital signal processing / computer vision
  • Experience with ROS, C++, Python.
  • Experience with Artificial Neural Network libraries or motivation to learn them
  • Motivation to yield a successful work

Kontakt

furkan.kaynar@tum.de

Betreuer:

Hasan Furkan Kaynar

Ingenieurpraxis

Recording of Robotic Grasping Failures

Beschreibung

The aim of this project is collecting data by robotic grasping experiments and creating a largescale labeled dataset. We will conduct experiments while attempting to grasp known or unknown objects autonomously. The complete pipeline includes:

- Estimating grasp poses via computer vision

- Robotic motion planning

- Executing the grasp physically

- Recording necessary data

- Organizing the recorded data into a well-structured dataset

 

Most of the data collection pipeline has been already developed, additions and modifications may be needed.

Voraussetzungen

Useful background:

- Digital signal processing

- Computer vision

- Dataset handling

 

Requirements:

- Experience with Python and ROS

- Motivation to yield a good outcome

Kontakt

furkan.kaynar@tum.de

 

(Please provide your CV and transcript in your application)

 

Betreuer:

Hasan Furkan Kaynar