One of the key problems in planning and control of redundant robots is the fast generation of controls when multiple tasks and constraints need to be satisfied. In the literature, this problem is classically solved by multi-task prioritized approaches, where the priority of each task is determined by a weight function, describing the task strict/soft priority. In this paper, we propose to leverage machine learning techniques to learn the temporal profiles of the task priorities, represented as parametrized weight functions: we automatically determine their parameters through a stochastic optimization procedure. We show the effectiveness of the proposed method on a simulated 7 DOF Kuka LWR and both a simulated and a real Kinova Jaco arm. We compare the performance of our approach to a state-of-the-art method based on soft task prioritization, where the task weights are typically hand-tuned.

Learning soft task priorities for control of redundant robots / Modugno, Valerio; Neumann, Gerard; Rueckert, Elmar; Oriolo, Giuseppe; Peters, Jan; Ivaldi, Serena. - ELETTRONICO. - (2016), pp. 221-226. ((Intervento presentato al convegno 2016 IEEE International Conference on Robotics and Automation, ICRA 2016 tenutosi a Stockholm; Sweden nel 2016 [10.1109/ICRA.2016.7487137].

Learning soft task priorities for control of redundant robots

MODUGNO, VALERIO
;
ORIOLO, Giuseppe;
2016

Abstract

One of the key problems in planning and control of redundant robots is the fast generation of controls when multiple tasks and constraints need to be satisfied. In the literature, this problem is classically solved by multi-task prioritized approaches, where the priority of each task is determined by a weight function, describing the task strict/soft priority. In this paper, we propose to leverage machine learning techniques to learn the temporal profiles of the task priorities, represented as parametrized weight functions: we automatically determine their parameters through a stochastic optimization procedure. We show the effectiveness of the proposed method on a simulated 7 DOF Kuka LWR and both a simulated and a real Kinova Jaco arm. We compare the performance of our approach to a state-of-the-art method based on soft task prioritization, where the task weights are typically hand-tuned.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/937011
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