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.

• 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

For vision-based robotic task planning, it is crucial to know the pose of the camera, in order to relate the points to the world coordinates for the robotic task. For this aim, there are many calibration techniques that require manual work. In this project, we will work on automatic hand-eye-calibration to make the calibration process easier.

- Experience with Python

- Basic knowledge of Digital Signal Processing /Image processing

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.

Useful background:

- Digital signal processing

- Computer vision

- Dataset handling

Requirements:

- Experience with Python and ROS

- Motivation to yield a good outcome