Anticipating many futures: Online human motion prediction and synthesis for human-robot collaboration

by   Judith Butepage, et al.
KTH Royal Institute of Technology

Fluent and safe interactions of humans and robots require both partners to anticipate the others' actions. A common approach to human intention inference is to model specific trajectories towards known goals with supervised classifiers. However, these approaches do not take possible future movements into account nor do they make use of kinematic cues, such as legible and predictable motion. The bottleneck of these methods is the lack of an accurate model of general human motion. In this work, we present a conditional variational autoencoder that is trained to predict a window of future human motion given a window of past frames. Using skeletal data obtained from RGB depth images, we show how this unsupervised approach can be used for online motion prediction for up to 1660 ms. Additionally, we demonstrate online target prediction within the first 300-500 ms after motion onset without the use of target specific training data. The advantage of our probabilistic approach is the possibility to draw samples of possible future motions. Finally, we investigate how movements and kinematic cues are represented on the learned low dimensional manifold.


page 1

page 7


Multimodal Deep Generative Models for Trajectory Prediction: A Conditional Variational Autoencoder Approach

Human behavior prediction models enable robots to anticipate how humans ...

Probabilistic Human Motion Prediction via A Bayesian Neural Network

Human motion prediction is an important and challenging topic that has p...

Dynamic Robot Motion Prediction Updates in Physical Human-Robot Interactive Tasks

Human-robot collaboration is on the rise. Robots need to increasingly im...

Learning Collision-free and Torque-limited Robot Trajectories based on Alternative Safe Behaviors

This paper presents an approach to learn online generation of collision-...

Anticipating Human Intention for Full-Body Motion Prediction in Object Grasping and Placing Tasks

Motion prediction in unstructured environments is a difficult problem an...

Deep representation learning for human motion prediction and classification

Generative models of 3D human motion are often restricted to a small num...

Motron: Multimodal Probabilistic Human Motion Forecasting

Autonomous systems and humans are increasingly sharing the same space. R...

Please sign up or login with your details

Forgot password? Click here to reset