Extended Force and Velocity Prediction in Human-Robot Collaborative Transportation through Future Environment Representation Estimation

Abstract

In this work, we address the challenge of predicting human-applied force and velocity during collaborative object transportation over extended distances (5–8 m). We enhance state-of-the-art predictors by refining their input data processing, which significantly improves prediction accuracy. Furthermore, we extend the temporal prediction horizon from 1 s to 2 s without compromising performance, by introducing an extra environmental prediction module that conditions force and velocity estimations based on anticipated sensory input. This integration captures the contextual dependency of human behaviour during joint transport. Experimental evaluations, both on dataset and in real-world settings, validate the effectiveness of our approach. Specifically, our best model manages to achieve success rates in testset of up to 90.4% in predicting the human’s exerted force and up to 93.0% in the velocity of the human-robot pair during the next 2 s, and up to 87.1% and 91.3% respectively in real experiments.