A new method to recognize the intention of a human worker while performing a collaborative task with a robot is proposed. For this purpose, two recurrent neural network (RNN) architectures capable of predicting the worker's target were developed. The first uses marker-based tracking of hand positions and the second RGB-D videos of human motion. The system was implemented to perform a collaborative assembly task. The results show high intention prediction accuracy for both networks, with accuracy increasing once a larger portion of human motion has been observed, making the proposed method viable for efficient and dynamic humanrobot collaboration. Furthermore, we developed a framework that enables online adaptation of robot trajectories based on estimated human intentions.

No abstract

Recent advances in deep neural networks have opened up new possibilities for visuomotor robot learning. In the context of human-robot or robot-robot collaboration, such networks can be trained to predict future poses and this information can be used to improve the dynamics of cooperative tasks. This is important, both in terms of realizing various cooperative behaviors, and for ensuring safety. In this article, we propose a recurrent neural architecture, capable of transforming variablelength input motion videos into a set of parameters describing a robot trajectory, where predictions can be made after receiving only a few frames. A simulation environment is utilized to expand the training database and to improve generalization capability of the network. The resulting architecture demonstrates good accuracy when predicting handover trajectories, with models trained on synthetic and real data showing better performance than when trained on real or simulated data only. The computed trajectories enable the execution of handover tasks with uncalibrated robots, which was verified in an experiment with two real robots.

No abstract