Wearable Exoskeleton Sensing for Robotic Manipulation Training
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Solution Overview
Problem
Existing robotic manipulation systems face challenges in collecting high-quality training data for developing adaptive manipulation 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, using multimodal data collection to train neural networks for robotic systems, incorporating pressure sensors, position sensors, cameras, and time-of-flight sensors, and leveraging augmented reality headsets for position tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional data collection methodologies are used for robotic manipulation training, then the process is simpler to implement, but the data quality and transfer fidelity are insufficient
Solution Approach 1:
The data collection system is segmented into multiple independent sensor modules (force sensors, tactile sensors, position sensors, cameras) distributed across the wearable exoskeleton and robotic manipulator. Each sensor type captures specific aspects of manipulation, and their data is integrated to form comprehensive training datasets, resolving the contradiction between data quality and system complexity
Solution Approach 2:
A wearable exoskeleton device serves as an intermediary between human operators and robotic manipulators during teleoperation. The exoskeleton captures human manipulation actions through integrated sensors and transmits this data to train robotic models, enabling high-fidelity data collection while maintaining a manageable system architecture through modular design
2Measurement precision
If multiple sensor types are integrated for comprehensive data collection, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The wearable exoskeleton is designed as a multi-functional platform that simultaneously supports multiple sensor types (force sensors, tactile sensors, position sensors, cameras) and serves multiple functions: capturing human manipulation actions, tracking robotic manipulator positions, and providing haptic feedback. This universal design consolidates what would otherwise be separate systems into one integrated solution
Solution Approach 2:
Multiple sensor types and data collection functions are merged into the wearable exoskeleton system. Force sensors, tactile sensors, position sensors, and cameras are combined in a unified hardware platform with integrated data processing, reducing the overall system complexity compared to using separate independent systems for each sensor type
3Adaptability or versatility
If teleoperation systems are used for data collection, then the ease of operation improves, but the scalability and intuitiveness are limited
Solution Approach 1:
The wearable exoskeleton system enables operators to collect manipulation data through natural human movements without requiring complex control interfaces. The exoskeleton automatically captures kinematic and force data during natural teleoperation, making the system intuitive to operate while highly scalable across different manipulation tasks and robotic platforms
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 solution enables more intuitive and efficient data collection, resulting in higher-fidelity training datasets that enhance robotic manipulation capabilities by improving spatial accuracy, reducing computational overhead, and optimizing sensor positioning, leading to more precise and reliable robotic control models.
Implementation Method 1
time-of-flight sensors to capture environmental 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.