Adapting Parameterized Motions using Iterative Learning and Online Collision Detection

Johan Sund Laursen, Lars Carøe Sørensen, Ulrik Pagh Schultz, Lars-Peter Ellekilde, Dirk Kraft

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


Achieving both the flexibility and robustness required to advance the use of robotics in small and medium-sized productions is an essential but difficult task.
A fundamental problem is making the robot run blindly without additional sensors while still being robust to uncertainties and variations in the assembly processes.
In this paper, we address the use of parameterized motions suitable for blind execution and robust to uncertainties in the assembly process.
Collisions and incorrect assemblies are detected based on robot motor currents while motion parameters are updated based on Bayesian Optimization utilizing Gaussian Process learning.
This allows for motion parameters to be optimized using real world trials which incorporate all uncertainties inherent in the assembly process without requiring advanced robot and sensor setups.
The result is a simple and straightforward system which helps the user automatically find robust and uncertainty-tolerant motions.
We present experiments for an assembly case showing both detection and learning in the real world and how these combine to a robust robot system.
Original languageEnglish
Title of host publicationProceeding of the 2018 IEEE International Conference on Robotics and Automation
Publication date13. Sept 2018
ISBN (Print)978-1-5386-3082-2
ISBN (Electronic)978-1-5386-3081-5
Publication statusPublished - 13. Sept 2018
Event2018 IEEE International Conference on Robotics and Automation - The Brisbane Convention & Exhibition Centre, Brisbane, Australia
Duration: 21. May 201825. May 2018


Conference2018 IEEE International Conference on Robotics and Automation
LocationThe Brisbane Convention & Exhibition Centre
Internet address

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