Neural Middleware for Device-Agnostic XR Haptic Control
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Solution Overview
Problem
There is a lack of a universal approach for managing different types of haptic devices used for extended reality (XR) applications, hindering the development of immersive XR experiences due to the unique functions, sensors, and actuators of these devices, and the challenge of combining haptics and gesture recognition across various client devices.
Innovation Solution
A generic middleware component utilizing a neural network to control haptic devices, adapting XR interactions based on client device capabilities, and employing machine learning algorithms like SVMs and CNNs to generate appropriate haptic feedback regardless of device-specific functionalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a universal approach for managing haptic devices is implemented, then adaptability across different device types is improved, but device complexity increases due to the need to handle unique functions, sensors, and actuators of each device type
Solution Approach 1:
The patent implements a universal haptic feedback system that can work across multiple device types (haptic gloves, controllers, suits) through a common architecture. The backend server provides device-agnostic haptic feedback generation, while a middleware layer adapts the feedback to specific device characteristics. This allows one system to serve multiple functions across different hardware platforms without requiring separate implementations for each device type.
Solution Approach 2:
The patent introduces a middleware layer that acts as an intermediary between the backend server and various haptic devices. This middleware handles the complexity of device-specific implementations by providing a standardized interface that translates generic haptic feedback commands into device-specific control signals. The middleware manages sensor data from different devices and converts it into a unified format for the backend server, while also translating server commands into appropriate device-specific outputs.
2Measurement precision
If device-specific haptic implementations are used, then haptic feedback accuracy for each device is improved, but ease of operation deteriorates as each XR application must implement features for all possible device combinations
Solution Approach 1:
The patent divides the haptic feedback system into distinct segments: a backend server that handles high-level haptic logic and feedback generation, a middleware layer that handles device-specific adaptations, and individual device drivers that manage specific hardware. This segmentation allows each component to focus on specific tasks - the backend maintains haptic accuracy through sophisticated algorithms while the middleware handles the complexity of device variations, making the system easier to operate.
Solution Approach 2:
The middleware serves as an intermediary that shields XR applications from device-specific complexities. Applications interact with a standardized haptic feedback interface, while the middleware translates these generic commands into device-specific implementations. This maintains haptic feedback accuracy by preserving the original feedback intent while adapting to different devices, without requiring applications to implement features for all possible device combinations.
3Ease of operation
If generic middleware with neural network is implemented, then ease of operation is improved by providing device-agnostic tools, but device complexity increases due to the need for neural network configuration and training
Solution Approach 1:
The neural network in the middleware is trained automatically through the interaction between the backend server and the haptic devices. The system collects sensor data and haptic feedback outcomes, and the neural network adapts its parameters through this feedback loop without requiring manual configuration. This self-service capability allows the generic middleware to automatically optimize for specific devices, reducing the operational burden while managing the complexity of neural network adaptation.
Solution Approach 2:
The neural network dynamically adjusts its parameters based on the specific haptic device being used and the user's interactions. Instead of requiring manual configuration for each device, the system changes the neural network's internal parameters through automated training processes. This allows the generic middleware to adapt to different devices by modifying parameters rather than requiring structural changes, improving ease of operation while managing complexity through automated parameter optimization.
Data Source
AI summary
Systems and methods are provided for configuring middleware to control a haptic device using a neural network. A system generates for display, based on at least one generic UI element, a virtual object within an XR environment. The XR application is configured to run on a local device, wherein the XR application causes display of the XR environment. The system configures middleware to run on the local device, wherein the middleware is configured to control at least one haptic device using at least one neural network. Based on avatar movement detected in the XR environment near the virtual object, the system causes the middleware to input control data into the at least one neural network. The at least one neural network outputs the control data for controlling the at least one haptic device. The system controls, by the middleware, the at least one haptic device based on the control data.