Trabajo presentado en la 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), celebrada en Nápoles (Italia) del 31 de agosto al 4 de septiembre de 2020 ; 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 infor-mation. 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.
Recurrent neural networks for inferring intentions in shared tasks for industrial collaborative robots
2020-01-01
Conference paper
Electronic Resource
English
DDC: | 629 |
On inferring intentions in shared tasks for industrial collaborative robots
BASE | 2019
|INFERRING DRIVER INTENTIONS USING A DRIVER MODEL BASED ON QUEUING NETWORK
British Library Conference Proceedings | 2013
|