This lecture introduces various approaches for autonomous grasp and manipulation planning and execution - from classical approaches to deep reinforcement learning methods (end-to-end learning). The usual grasping pipeline covers scene segmentation, object fitting, grasp selection, grasp evaluation, planning, and execution. If time admits we will also study tactile sensing and tactile-based manipulation - an important research topic nowadays.
As a theoretical foundation to tackle these topics, we will first study classical grasp evaluation based on wrench-space analysis: We will consider how to formally describe grasps, how to model contacts and friction, as well as learn about grasp evaluation measures like grasp stability and manipulability.
Tentative Schedule:
The lecture will be given in English if desired by the audience. The slides will be in English.
required prerequisites: Robotic Manipulators
(Robotics Basics: Transforms, Forward/Inverse Kinematics, Velocities/Twists, Jacobian IK)
Rhythmus | Tag | Uhrzeit | Format / Ort | Zeitraum |
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Modul | Veranstaltung | Leistungen | |
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39-M-Inf-MI Manuelle Intelligenz | Autonomes Greifen | unbenotete Prüfungsleistung
benotete Prüfungsleistung |
Studieninformation |
Die verbindlichen Modulbeschreibungen enthalten weitere Informationen, auch zu den "Leistungen" und ihren Anforderungen. Sind mehrere "Leistungsformen" möglich, entscheiden die jeweiligen Lehrenden darüber.
At least 50% of the exercises need to be solved.
The (oral) exam will cover topics from both, the lecture and exercises.
Zu dieser Veranstaltung existiert ein Lernraum im E-Learning System. Lehrende können dort Materialien zu dieser Lehrveranstaltung bereitstellen: