In this paper, we analyze the possibilities offered by Deep Learning State-of-the-Art architectures such as Transformers and Visual Transformers in generating a prediction of the human's force in a Human-Robot collaborative object transportation task at a middle distance. We outperform our previous predictor by achieving a success rate of 93.8% in testset and 90.9% in real experiments with 21 volunteers predicting in both cases the force that the human will exert during the next 1 s. A modification in the architecture allows us to obtain a second output from the model with a velocity prediction, which allows us to improve the capabilities of our predictor if it is used to estimate the trajectory that the human-robot pair will follow. An ablation test is also performed to verify the relative contribution to performance of each input.

Work supported under the European project CANOPIES (H2020- ICT-2020-2-101016906) and by JST Moonshot R & D Grant Number: JPMJMS2011-85. The first author acknowledges Spanish FPU grant with ref. FPU19/06582.

Peer reviewed