FlexRoP – flexible, assistive robots for customized production

Ikeda, Markus; Maddukuri, Srinivas; Hofmann, Michaël; Pichler, Andreas; Xiang, Zhang; Polydoros, Athanasios; Piater, Justus; Winkler, Klemens; Brenner, Klaus; Harton, Ioan; Neugebauer, Uwe

doi:10.15203/3187-22-1-11

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…In the factory, the company is exploring the possibilities of teaching a robot by human demonstration or coaching. [1] The main technical obstacle is how robots could conveniently capture, reproduce and generalize the complex manipulation on objects especially with physical constraints For a robot executing tasks, the generalization is applied in two-fold. Firstly, the robot should be able to adapt itself to different environments, e.g.…”

Section: Introductionmentioning

confidence: 99%

Learning Descriptor of Constrained Task from Demonstration

Zhang,

Saveriano,

Piater

2021

Preprint

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Constrained objects, such as doors and drawers are often complex and share a similar structure in the human environment. A robot needs to interact accurately with constrained objects to safely and successfully complete a task. Learning from Demonstration offers an appropriate path to learn the object structure of the demonstration for unknown objects for unknown tasks. There is work that extracts the kinematic model from motion. However, the gap remains when the robot faces a new object with a similar model but different contexts, e.g. size, appearance, etc. In this paper, we propose a framework that integrates all the information needed to learn a constrained motion from a depth camera into a descriptor of the constrained task. The descriptor consists of object information, grasping point model, constrained model, and reference frame model. By associating constrained learning and reference frame with the constrained object, we demonstrate that the robot can learn the book opening model and parameter of the constraints from demonstration and generalize to novel books.

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Section: Introductionmentioning

confidence: 99%