Publication
Recurrent neural networks for inferring intentions in shared tasks for industrial collaborative robots
Conference Article
Conference
IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)
Edition
29th
Pages
665-670
Doc link
http://dx.doi.org/10.1109/RO-MAN47096.2020.9223587
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Authors
Projects associated
Abstract
Industrial robots are evolving to work closely with humans in shared spaces. Hence, robotic tasks are increasingly shared between humans and robots in collaborative settings. To enable a fluent human robot collaboration, robots need to predict and respond in real-time to worker's intentions. We present a method for early decision using force information. Forces are provided naturally by the user through the manipulation of a shared object in a collaborative task. The proposed algorithm uses a recurrent neural network to recognize operator's intentions. The algorithm is evaluated in terms of action recognition on a force dataset. It excels at detecting intentions when partial data is provided, enabling early detection and facilitating a quick robot reaction.
Categories
generalisation (artificial intelligence), industrial robots, learning (artificial intelligence).
Scientific reference
M. Maceira, A. Olivares-Alarcos and G. Alenyà. Recurrent neural networks for inferring intentions in shared tasks for industrial collaborative robots, 29th IEEE International Symposium on Robot and Human Interactive Communication, 2020, Naples, Italy (Virtual), pp. 665-670.
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