Wearable Hand Exoskeleton for High-Fidelity Robot Training Data
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Solution Overview
Problem
Existing robotic manipulation systems struggle with collecting high-quality training data for developing adaptive manipulation capabilities, facing challenges in intuitiveness, data quality, transfer fidelity, and scalability, despite advances in sensor technologies and learning algorithms.
Innovation Solution
A wearable exoskeleton device with integrated sensors captures human hand movements and environmental interactions, collecting multimodal data for training neural networks to control robotic systems, using pressure sensors, position sensors, cameras, and time-of-flight sensors, along with mobile devices or AR headsets for position tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional teleoperation systems and demonstration approaches are used for data collection, then robotic manipulation systems can be trained, but the data quality, intuitiveness, and transfer fidelity are insufficient
Solution Approach 1:
The wearable exoskeleton device creates a physical copy of the human hand with identical degrees of freedom and sensor placement. This anatomical replica captures human manipulation data with high fidelity while maintaining natural intuitiveness, as the device directly replicates human hand movements without requiring complex teleoperation interfaces or demonstration protocols
Solution Approach 2:
The patent replaces conventional teleoperation mechanical interfaces with a wearable exoskeleton that uses direct mechanical coupling to the human hand. This substitution eliminates the need for controllers, screens, and complex操作 interfaces, allowing natural hand movements to be directly translated into robotic control signals with high data quality
2Measurement precision
If multiple sensors are integrated to capture multidimensional data, then data fidelity improves, but device complexity increases
Solution Approach 1:
The wearable exoskeleton device integrates multiple sensor types (force sensors, torque sensors, position sensors, accelerometers, gyroscopes) into a single multi-functional platform. Each sensor serves multiple purposes: force and torque sensors capture both manipulation forces and contact forces, while position sensors track both joint angles and hand position, reducing overall system complexity through functional consolidation
Solution Approach 2:
The patent merges multiple sensing modalities (force, torque, position, acceleration, orientation) into a unified sensor framework mounted on the exoskeleton. This consolidation allows simultaneous capture of multidimensional manipulation data from a single device rather than requiring separate sensor systems, thereby improving data fidelity without proportionally increasing device complexity
3Productivity
If wearable exoskeleton device with multiple sensors is used, then data collection efficiency improves, but manufacturing complexity increases
Solution Approach 1:
The wearable exoskeleton is divided into modular segments corresponding to each finger and the palm, with sensors distributed across these segments. This segmentation allows independent manufacturing and assembly of each module, simplifying the overall manufacturing process while maintaining high data collection efficiency through comprehensive sensor coverage
Solution Approach 2:
The patent uses standardized sensor interfaces and modular mechanical connections that allow easy assembly and disassembly. By standardizing mounting parameters and connection protocols across different sensor types and exoskeleton modules, the manufacturing complexity is reduced despite the multiple sensors integrated into the system
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
Enables intuitive and efficient data collection, improving robotic manipulation capabilities by enhancing data fidelity, spatial accuracy, and reducing computational overhead, resulting in precise and reliable robotic control models.
Implementation Method 1
The plurality of sensors mounted on the device may include, but is not limited to, pressure sensors
Implementation Method 2
angle data from position sensors at the joints
Implementation Method 3
visual data from cameras
Implementation Method 4
distance data from time-of-flight sensors
Implementation Method 5
contact sound data from piezoelectric microphones
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.