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, using multimodal data collection to train neural networks for robotic systems, incorporating pressure sensors, position sensors, cameras, and time-of-flight sensors, along with AR headsets for spatial tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional data collection methodologies are used, then implementation is simpler, but data quality and transfer fidelity deteriorate
Solution Approach 1:
The wearable device segments data collection into multiple independent sensor modalities (ToF sensors for depth, pressure sensors for contact forces, position sensors for joint angles, cameras for visual context). Each sensor type captures a specific aspect of manipulation, and their outputs are integrated to form comprehensive training data. This segmentation allows high-quality multi-modal data collection while keeping each sensor component relatively simple and manageable.
Solution Approach 2:
The wearable exoskeleton acts as an intermediary between human operators and robotic systems. It captures human manipulation actions through integrated sensors and translates them into training data for robotic models. This intermediary role enables high-fidelity transfer of human dexterity and tactile sensing capabilities to robotic systems without requiring direct human-robot interaction, thereby improving data quality while maintaining practical system implementation.
2Measurement precision
If comprehensive multimodal sensors are integrated, then data fidelity improves, but device complexity increases
Solution Approach 1:
The patent merges multiple sensor types (ToF, pressure, position, cameras) into a single integrated wearable exoskeleton device. This consolidation allows comprehensive multimodal data collection from a unified platform, improving overall data fidelity while reducing the complexity of coordinating separate systems. The sensors are spatially distributed across the hand and finger elements but managed as an integrated system with synchronized data output.
Solution Approach 2:
The wearable exoskeleton device performs multiple functions simultaneously: it tracks hand pose, measures contact forces, captures depth information, and records visual context. This multi-functionality is achieved through a unified device architecture where each sensor type serves both its specific measurement function and contributes to the overall manipulation understanding. The device thus achieves high data fidelity across multiple dimensions without requiring separate specialized systems.
3Measurement precision
If time-of-flight sensors are used, then spatial accuracy improves, but computational overhead increases
Solution Approach 1:
The ToF sensors perform preliminary spatial mapping and depth estimation before manipulation tasks begin. By pre-capturing the 3D environment and object locations, the system reduces the computational burden during actual manipulation. The spatial accuracy from ToF data is used to pre-position robotic actuators and plan trajectories, minimizing real-time computational requirements while maintaining high spatial precision.
Solution Approach 2:
The patent replaces complex mechanical vision systems with optical time-of-flight sensing for depth measurement. ToF sensors use light propagation time rather than mechanical scanning or complex lens systems, achieving high spatial accuracy with reduced computational overhead. This substitution maintains precise spatial measurement capabilities while simplifying the overall sensing architecture and reducing processing requirements compared to traditional vision-based depth estimation.
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
Enhances data fidelity and spatial accuracy, reduces computational overhead, and improves robotic control models by creating higher-fidelity training datasets, enabling more precise and reliable robotic manipulation.
Implementation Method 1
each comprising a light emitter configured to emit light, a grid of light receivers configured to detect reflections of the light, and circuitry configured to collect time-of-flight data representing the time between emission of the light and detection of the reflections
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.