Prediction of Human Whole-Body Movements with AE-ProMPs - Archive ouverte HAL Access content directly
Conference Papers Year : 2018

Prediction of Human Whole-Body Movements with AE-ProMPs


The ability to predict the future intended movement is crucial for collaborative robots to anticipate the human actions and for assistive technologies to alert if a particular movement is non-ergonomic and potentially dangerous for the human health. In this paper, we address the problem of predicting the future human whole-body movements given early observations. We propose to predict the continuation of the high-dimensional trajectories mapped into a reduced latent space, using autoencoders (AE). The prediction is based on a probabilistic description of the movement primitives (ProMPs) in the latent space, which notably reduces the computational time for the prediction to occur, and hence enables to use the method in real-time applications. We evaluate our method, named AE-ProMPs, for predicting future movements belonging to a dataset of 7 different actions performed by a human, recorded by a wearable motion tracking suit.
Fichier principal
Vignette du fichier
humanoids_AE_ProMPs_review.pdf (8.75 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-01895148 , version 1 (14-10-2018)


  • HAL Id : hal-01895148 , version 1


Oriane Dermy, Maxime Chaveroche, Francis Colas, François Charpillet, Serena Ivaldi. Prediction of Human Whole-Body Movements with AE-ProMPs. IEEE-RAS 18th International Conference on Humanoid Robots (HUMANOIDS 2018), Nov 2018, Beijing, China. pp.572-579. ⟨hal-01895148⟩
235 View
309 Download


Gmail Mastodon Facebook X LinkedIn More