GAN-based Material Tuning

Tool Summary

General Purpose Information
Year of First Releaseⓘ The year a tool was first publicly released or discussed in an academic paper.	2020
Platformⓘ The OS or software framework needed to run the tool.	iPad
Availabilityⓘ If the tool can be obtained by the public.	Unavailable
Licenseⓘ Tye type of license applied to the tool.	Unknown
Venueⓘ The venue(s) for publications.	IEEE ACCESS
Intended Use Caseⓘ The primary purposes for which the tool was developed.	Prototyping

Hardware Control Information
Haptic Categoryⓘ The general types of haptic output devices controlled by the tool.	Vibrotactile
Hardware Abstractionⓘ How broad the type of hardware support is for a tool. Bespoke: Hardware is custom built, including open hardware. Consumer: Support is limited to a specific commercial device or a family of devices. Class: Any device with the same affordances can be used, possibly with some configuration required.	Class
Device Namesⓘ The hardware supported by the tool. This may be incomplete.	Voice Coil
Body Positionⓘ Parts of the body where stimuli are felt, if the tool explicitly shows this.	Hand

Interaction and Interface Information
Driving Featureⓘ If haptic content is controlled over time, by other actions, or both.	Action
Effect Localizationⓘ How the desired location of stimuli is mapped to the device. Device-centric: No spatial information is taken into acccount. Actuators are directly controlled. Target-centric: Stimuli are placed on the body or are assigned to a virtual object to be triggered through interaction. The tool handles how the device will display the effect. Location-aware: The desired location of an effect is taken into account to some extent, but is ultimately presented in context of the output device.	Location-aware
Media Supportⓘ Support for non-haptic media in the workspace, even if just to aid in manual synchronization.	None
Iterative Playbackⓘ If haptic effects can be played back from the tool to aid in the design process.	Yes
Design Approachesⓘ Broadly, the methods available to create a desired effect. Direct parametric control (DPC): low-level parameters are directly modifiable. Process: parameters are controllable by an abstract process. Sequencing: reusable effects are ordered in time to create complex effects. Library: a library of pre-existing effects is available for use or re-use. Description: a natural language description of the experience is used to find an appropriate effect, often through searching a library.	Process
Interaction Metaphorsⓘ Common UI metaphors that define how a user interacts with a tool. Track: a timeline represents an interactive channel containing effects. Keyframe: key points of the effect are set and behavior between them is interpolated. Score: an adaptation of a musical score or notation represents haptic effects. Dataflow: a dataflow programming model is used to control haptic output. Demonstration: physical actions or other data are mapped simply to effects or parameters. Generic Menu: typical GUI elements (e.g., sliders) are used to control effects with the absence of other metaphors.	N/A
Storageⓘ How data is stored for import/export or internally to the software.	None
Connectivityⓘ How the tool can be extended to support new data, devices, and software.	None

Additional Information

The tuning method developed here uses a generative adversarial network (GAN) to produce vibrations corresponding to materials with properties in between those provided in the dataset used to train the GAN. The application described in the paper was intended to test this method of haptic tuning with users.

For more information about this method, consult the 2020 IEEE Access paper.