A Riemannian Take on Human Motion Analysis and Retargeting

TL;DR

This paper introduces a Riemannian geometric model for analyzing and retargeting human motion, revealing that human movements can be viewed as sequences of geodesic synergies that optimize energy and can be transferred to robots.

Contribution

It proposes a novel Riemannian geometry-based framework for modeling human motion as geodesic synergies, enabling better understanding and robotic reproduction of human movements.

Findings

Model segments human motions into geodesic synergies.

Accurately predicts arm postures and trajectories.

Enables transfer of human motions to robots.

Abstract

Dynamic motions of humans and robots are widely driven by posture-dependent nonlinear interactions between their degrees of freedom. However, these dynamical effects remain mostly overlooked when studying the mechanisms of human movement generation. Inspired by recent works, we hypothesize that human motions are planned as sequences of geodesic synergies, and thus correspond to coordinated joint movements achieved with piecewise minimum energy. The underlying computational model is built on Riemannian geometry to account for the inertial characteristics of the body. Through the analysis of various human arm motions, we find that our model segments motions into geodesic synergies, and successfully predicts observed arm postures, hand trajectories, as well as their respective velocity profiles. Moreover, we show that our analysis can further be exploited to transfer arm motions to robots by reproducing individual human synergies as geodesic paths in the robot configuration space.