Internal models for object manipulation: Determining optimal contact locations

Loading...
Thumbnail Image

View/Download File

Persistent link to this item

Statistics
View Statistics

Journal Title

Journal ISSN

Volume Title

Title

Internal models for object manipulation: Determining optimal contact locations

Published Date

2006-02-13

Publisher

Type

Report

Abstract

Although there are an infinite number of ways that humans can lift an object, they tend to reach in a predictable manner. This suggests that people are aware of, and optimizing, some sort of loss function. This paper outlines a natural loss function that may be used to predict people's actions in everyday reaching tasks. The loss function is based on the physics of object manipulation and the assumption that people are planning for the intended motion of the object. Using this framework, we are able to make predictions about how people should reach if they are minimizing their expected risk. To test the model, we required people to reach to objects at varying orientations. Our experimental results indicate that people are reaching in a manner that minimizes their expected risk for the task. These findings suggest that people planning for the intended motion of the object and that our brain is aware of the physics involved with object manipulation.

Keywords

Description

Related to

Replaces

License

Series/Report Number

Funding information

Isbn identifier

Doi identifier

Previously Published Citation

Suggested citation

Schrater, Paul; Schlicht, Erik J.. (2006). Internal models for object manipulation: Determining optimal contact locations. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/215688.

Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.