Spatio-Temporal Modeling of Grasping Actions

• Understanding the spatial dimensionality and temporal context of human hand actions can provide representations for programming grasping actions in robots and inspire design of new robotic and prosthetic hands. The natural representation of human hand motion has high dimensionality. For specific activities such as handling and grasping of objects, the commonly observed hand motions lie on a lower-dimensional non-linear manifold in hand posture space. Although full body human motion is well studied within Computer Vision and Biomechanics, there is very little work on the analysis of hand motion with nonlinear dimensionality reduction techniques. In this paper we use Gaussian Process Latent Variable Models (GPLVMs) to model the lower dimensional manifold of human hand motions during object grasping. We show how the technique can be used to embed high-dimensional grasping actions in a lower-dimensional space suitable for modeling, recognition and mapping.

Nyckelord

Dimensional spaces

Full body

Gaussian Processes

Hand motion

Hand posture

High dimensionality

High-dimensional

Human hand motions

Human hands

Human motions

Latent variable models

Lower dimensional manifolds

Natural representation

Nonlinear dimensionality reduction

Nonlinear manifolds

Object grasping

Prosthetic hands

Spatial dimensionalities

Spatiotemporal modeling

Specific activity

Biomechanics

Intelligent robots

Machine design

Robot programming

TECHNOLOGY

TEKNIKVETENSKAP

Publikations- och innehållstyp

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