Teaching robots to physically operate locks and mechanisms
©SCIoI
We will develop a novel approach to Learning from Demonstration for teaching a robot complex, contact-rich manipulation tasks. This approach will enable the robots to physically operate complex locks and other mechanisms. The scientific challenge consists of reliably operating such multi-degree-of-freedom mechanisms that require transitions between different multi-contact situations. Rather than programming these manipulation actions directly, we will have a human demonstrate motions to the robot. We will use our knowledge about the problem and a clever way to elicit demonstration behavior to produce robust and transferable manipulation strategies. These strategies will form an important building block for the one of the SCIoI demonstrators in which a mobile manipulator has to escape from an escape room. To develop and validate the proposed approach to Learning from Demonstration, we will construct escape rooms of increasing mechanical complexity, matching the newly posed challenges to further advances in our approach.
Related Publications +
2756394
proj028
apa
50
creator
desc
year
20197
https://www.scienceofintelligence.de/wp-content/plugins/zotpress/
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Li, X., Baum, M., & Brock, O. (2023). Augmentation Enables One-Shot Generalization In Learning From Demonstration for Contact-Rich Manipulation.
IROS 2023.
https://doi.org/10.1109/IROS55552.2023.10341625
Li, X., & Brock, O. (2022). Learning from Demonstration Based On Environmental Constraints.
IEEE Robotics and Automation Letters with IROS Option.
https://doi.org/10.1109/LRA.2022.3196096
