In the project, you will work with robot models in simulated environments, e.g. [1, 2, 3, 4], and build a controller for manipulation of objects using general-purpose deep reinforcement learning algorithms [5], modern neural network architectures and classic approaches. You will experiment with adding touch sensors [6] to the hand and explore generalization of learned policies over different objects with and without tactile information.
[1] OpenAI Robotics
http://gym.openai.com/envs/#robotics
https://drive.google.com/open?id=1J2H92AstGpcFYqjmVymMSRKI5xISdB1b
[2] Robot open-Ended Autonomous Learning https://www.aicrowd.com/challenges/neurips-2019-robot-open-ended-autonomous-learning
[3] Differentiable Physics and Stable Modes for Tool-Use and Manipulation Planning
https://youtu.be/-L4tCIGXKBE
[4] MoveIt
https://moveit.ros.org
https://youtu.be/0og1SaZYtRc
[5] OpenAI Deep Reinforcement Learning Baselines
https://github.com/openai/baselines
[6] Touch sensors
https://rebrand.ly/TouchSensors
Introduction to Neural Networks or Advanced Neural Networks courses.
Python or C++ ( > 1 year).
In case this would not find enough interest for a team project, this project proposal would be also offered (in reduced/modified form)
Rhythmus | Tag | Uhrzeit | Format / Ort | Zeitraum |
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Modul | Veranstaltung | Leistungen | |
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39-M-Inf-GP Grundlagenprojekt Intelligente Systeme | Gruppenprojekt | unbenotete Prüfungsleistung
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Studieninformation |
Die verbindlichen Modulbeschreibungen enthalten weitere Informationen, auch zu den "Leistungen" und ihren Anforderungen. Sind mehrere "Leistungsformen" möglich, entscheiden die jeweiligen Lehrenden darüber.