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Solving Assembly Tasks via Deep Learning
The goal of the project is to enable precise positioning of the objects in 6D space using RGB camera as a main source of feedback.
Keywords: Robotics, Deep Learning, Robot Arms
Robotic arms can precisely position objects in space. This enables robots to do complex assembly tasks in factory settings where position of all objects is precisely known. However, in home environments, where the configuration of objects is constantly changing, this is still an open problem. Recent methods from computer vision enable to determine position and orientation of objects in the scene. This opens up possibilities to do assembly tasks in home environments and in collaboration with humans. Current methods rely on the facts that objects are fully visible and that the scene is static. However, when working with big objects or in collaboration with humans these assumptions are not valid anymore. The goal of this project is to investigate possibilities to overcome these issues. State-of-the-art methods need to be extended to work in a closed loop setting and to cases when objects are only partially visible.
Robotic arms can precisely position objects in space. This enables robots to do complex assembly tasks in factory settings where position of all objects is precisely known. However, in home environments, where the configuration of objects is constantly changing, this is still an open problem. Recent methods from computer vision enable to determine position and orientation of objects in the scene. This opens up possibilities to do assembly tasks in home environments and in collaboration with humans. Current methods rely on the facts that objects are fully visible and that the scene is static. However, when working with big objects or in collaboration with humans these assumptions are not valid anymore. The goal of this project is to investigate possibilities to overcome these issues. State-of-the-art methods need to be extended to work in a closed loop setting and to cases when objects are only partially visible.
Each year the IDEA League offers the students of its partner universities over 180 monthly grants for a short-term research exchange. In general, these grants are awarded based on academic merit. For more information visit http://idealeague.org/student-grant/
Master Thesis
CLS Student Project [managed by Max Planck ETH Center for Learning Systems]