Learning context-adaptive task constraints for robotic manipulation
Dennis Mronga, Frank Kirchner
In Robotics and Autonomous Systems, Elsevier, volume 141, pages 103779-103779, 2021.

Abstract :

Constraint-based control approaches offer a flexible way to specify robotic manipulation tasks and execute them on robots with many degrees of freedom. However, the specification of task constraints and their associated priorities usually requires a human-expert and often leads to tailor-made solutions for specific situations. This paper presents our recent efforts to automatically derive task constraints for a constraint-based robot controller from data and adapt them with respect to previously unseen situations (contexts). We use a programming-by-demonstration approach to generate training data in multiple variations (context changes) of a given task. From this data we learn a probabilistic model that maps context variables to task constraints and their respective soft task priorities. We evaluate our approach with 3 different dual-arm manipulation tasks on an industrial robot and show that it performs better than comparable approaches with respect to reproduction accuracy in previously unseen contexts.

Keywords :

Context-adaptive control, Constraint-based robot control, Programming-by-demonstration, Gaussian mixture regression, Dual-arm manipulation

Files:

arxiv_preprint_Learning_Context_adaptive_task_constraints_for_robotic_manipulation.pdf

Links:

https://authors.elsevier.com/a/1cwFq3HdG3ddl6


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