Wearable Hand Exoskeleton Sensing for Robotic Training Fidelity
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Solution Overview
Problem
Existing robotic manipulation systems face challenges in collecting high-quality training data for developing adaptive capabilities, as conventional methodologies struggle with intuitiveness, data quality, transfer fidelity, and scalability, and there is a significant gap between human and robotic dexterity and tactile sensing.
Innovation Solution
A wearable exoskeleton device with integrated sensors captures human hand movements and environmental interactions, collecting multimodal data through pressure sensors, position sensors, cameras, time-of-flight sensors, and piezoelectric microphones, paired with position tracking technologies to train neural networks for robotic systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional data collection methodologies (teleoperation, demonstration) are used, then robotic manipulation systems can be trained, but the data quality, intuitiveness, and transfer fidelity are insufficient
Solution Approach 1:
The patent uses wearable exoskeleton devices that copy human hand movements and tactile interactions to generate training data for robotic systems. The exoskeleton captures human manipulation behaviors and translates them into robotic control commands, creating high-fidelity training datasets that preserve the nuances of human dexterity while being collectible at scale
Solution Approach 2:
The patent replaces conventional teleoperation interfaces with wearable exoskeletons equipped with tactile sensors. This substitution captures not only positional data but also force, pressure, and tactile feedback information, significantly improving data quality and transfer fidelity from human to robotic systems
2Reliability
If human-level manipulation capabilities are to be achieved, then sophisticated tactile sensing and dexterity are required, but current sensor technologies and data collection methods are insufficient
Solution Approach 1:
The wearable exoskeleton device integrates multiple sensor types (force sensors, tactile arrays, position sensors, inertial measurement units) into a single multi-functional system. This unified approach captures comprehensive manipulation data including contact forces, surface textures, and motion trajectories, enabling reliable training of robotic manipulation capabilities without requiring separate specialized systems
Solution Approach 2:
The exoskeleton acts as an intermediary between human operators and robotic systems, translating human tactile sensations and manipulation intentions into robotic control commands. This intermediary layer captures rich tactile feedback information that bridges the gap between human dexterity and robotic precision
3Measurement precision
If comprehensive training datasets are collected to improve robotic manipulation, then data fidelity and spatial accuracy must be high, but collection efficiency and scalability are reduced
Solution Approach 1:
The wearable exoskeleton system enables operators to collect high-fidelity manipulation data during their natural, everyday interactions with objects. The system automatically captures position, force, and tactile information without requiring specialized training or deliberate data collection procedures, making high-precision data collection as efficient as normal human operation
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The wearable device enables intuitive and efficient data collection, improving data fidelity, spatial accuracy, and signal-to-noise ratio, resulting in higher-fidelity training datasets for more precise and reliable robotic control models.
Implementation Method 1
at least one piezoelectric microphone mounted on the device to detect vibrations caused by contact between the wearable device and objects and convert the vibrations into electrical signals representing contact sound data
Data Source
AI summary
Technology disclosed herein includes a wearable data collection device for training robotic systems. In an implementation, a wearable data collection device includes a hand element configured to receive a user's hand, multiple finger elements extending from the hand element, and joints coupling the finger elements to the hand element. The finger elements are constrained to movements that match capabilities of a robotic counterpart device. Multiple sensors mounted on the device capture pressure, position, visual, proximity, and acoustic data during recording sessions. The device may integrate with position tracking technologies such as mobile devices or augmented reality headsets. Data collected through the wearable device serves as training input for a neural network that controls the robotic counterpart.