Fulfilling the ISO/TS 15066 regulation is crucial for implementing a certifiable human-robot collaborative application. If not properly embedded in the definition of the control action for the robot, the application of ISO/TS 15066 requirements can lead to a conservative and inefficient behavior of the robot. In order to maximize the performance, in this paper we propose an approach based on Deep Reinforcement Learning (DRL) for integrating the safety standards in a collaborative application. The proposed strategy is experimentally validated.
An actor-critic strategy for a safe and efficient human robot collaboration / Gabrielli, G.; Secchi, C.. - (2021), pp. 919-926. (Intervento presentato al convegno 20th International Conference on Advanced Robotics, ICAR 2021 tenutosi a svn nel 2021) [10.1109/ICAR53236.2021.9659462].
An actor-critic strategy for a safe and efficient human robot collaboration
Gabrielli G.;Secchi C.
2021
Abstract
Fulfilling the ISO/TS 15066 regulation is crucial for implementing a certifiable human-robot collaborative application. If not properly embedded in the definition of the control action for the robot, the application of ISO/TS 15066 requirements can lead to a conservative and inefficient behavior of the robot. In order to maximize the performance, in this paper we propose an approach based on Deep Reinforcement Learning (DRL) for integrating the safety standards in a collaborative application. The proposed strategy is experimentally validated.
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