Multi-task prioritized controllers are able to generate complex robot behaviors that concurrently satisfy several tasks and constraints. To perform, they often require a human expert to define the evolution of the task priorities in time. In a previous paper [1] we proposed a framework to automatically learn the task priorities thanks to a stochastic optimization algorithm (CMA-ES) maximizing the robot performance on a certain behavior. Here, we learn the task priorities that maximize the robot performance, ensuring that the optimized priorities lead to safe behaviors that never violate any of the robot and problem constraints. We compare three constrained variants of CMA-ES on several benchmarks, among which two are new robotics benchmarks of our design using the KUKA LWR. We retain (1+1)-CMA-ES with covariance constrained adaptation [2] as the best candidate to solve our problems, and we show its effectiveness on two whole-body experiments with the iCub humanoid robot.

Learning soft task priorities for safe control of humanoid robots with constrained stochastic optimization / Modugno, Valerio; Chervet, Ugo; Oriolo, Giuseppe; Ivaldi, Serena. - ELETTRONICO. - (2016), pp. 101-108. (Intervento presentato al convegno 16th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2016 tenutosi a Cancun; Mexico nel 2016) [10.1109/HUMANOIDS.2016.7803261].

Learning soft task priorities for safe control of humanoid robots with constrained stochastic optimization

MODUGNO, VALERIO
;
ORIOLO, Giuseppe;
2016

Abstract

Multi-task prioritized controllers are able to generate complex robot behaviors that concurrently satisfy several tasks and constraints. To perform, they often require a human expert to define the evolution of the task priorities in time. In a previous paper [1] we proposed a framework to automatically learn the task priorities thanks to a stochastic optimization algorithm (CMA-ES) maximizing the robot performance on a certain behavior. Here, we learn the task priorities that maximize the robot performance, ensuring that the optimized priorities lead to safe behaviors that never violate any of the robot and problem constraints. We compare three constrained variants of CMA-ES on several benchmarks, among which two are new robotics benchmarks of our design using the KUKA LWR. We retain (1+1)-CMA-ES with covariance constrained adaptation [2] as the best candidate to solve our problems, and we show its effectiveness on two whole-body experiments with the iCub humanoid robot.
2016
16th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2016
Machine learning; Humanoids robot; whole body control;
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Learning soft task priorities for safe control of humanoid robots with constrained stochastic optimization / Modugno, Valerio; Chervet, Ugo; Oriolo, Giuseppe; Ivaldi, Serena. - ELETTRONICO. - (2016), pp. 101-108. (Intervento presentato al convegno 16th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2016 tenutosi a Cancun; Mexico nel 2016) [10.1109/HUMANOIDS.2016.7803261].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/937002
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