Wearable Hand Exoskeleton for Multimodal Robot Training Data
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Solution Overview
Problem
Existing robotic manipulation systems face challenges in collecting high-quality training data and sensor integration, and generalization across diverse tasks, with conventional methodologies like teleoperation systems and demonstration approaches facing limitations in data collection and intuitiveness, intuitiveness, and scalability, particularly in the field of robotic manipulation systems.
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 piezoelectric microphones, and AR headsets for intuitive and efficient data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional teleoperation systems and demonstration approaches are used for data collection, then the system is simpler to implement, but the data quality, intuitiveness, and scalability are limited
Solution Approach 1:
The system segments data collection into multiple modalities (force/torque sensors, tactile sensors, position tracking, audio recordings) distributed across wearable devices on the human operator and the robotic system. Each sensor type captures specific aspects of manipulation, and the segmented data streams are integrated to form comprehensive training datasets, thereby improving data quality while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The wearable data collection system is designed with multi-functionality to capture diverse manipulation data across various tasks and environments. The same sensor suite (force sensors, tactile arrays, position trackers) serves multiple purposes: recording human demonstrations, measuring contact forces, tracking spatial positions, and capturing audio cues. This universal approach enables scalable data collection for different robotic manipulation tasks without requiring task-specific hardware modifications
2Adaptability or versatility
If hard-coded control algorithms are used for robotic manipulation, then the control system is simpler, but the system struggles with novel objects and environmental variability
Solution Approach 1:
The system performs preliminary data collection using wearable sensors to capture human manipulation demonstrations before the robotic system encounters novel objects or environments. These pre-collected datasets include force patterns, tactile feedback, and position trajectories that serve as prior knowledge. When the robotic system encounters new situations, it references these pre-collected patterns to generalize its control strategy, thereby improving adaptability without requiring complex real-time reasoning algorithms
Solution Approach 2:
The system introduces machine learning models as intermediaries between the raw sensor data from wearable devices and the robotic control commands. These intermediary models (reinforcement learning agents, imitation learning networks) process the multi-modal sensor data and translate it into adaptable control policies. This intermediary layer enables the robotic system to generalize to novel objects and environments by learning from human demonstrations, while keeping the underlying control architecture manageable through standardized model interfaces
3Loss of information
If existing sensor technologies are used separately, then each sensor provides specific information, but the system lacks comprehensive manipulation data
Solution Approach 1:
The sensor system employs a nested architecture where smaller sensors are embedded within larger sensor assemblies. For example, tactile sensor arrays are nested within force sensor housings, and position tracking markers are nested on the robotic manipulator segments. This nested doll structure allows multiple sensor types to occupy the same physical space efficiently, reducing overall system complexity while capturing comprehensive manipulation data from multiple modalities simultaneously
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 data collection device enhances data fidelity, spatial accuracy, and signal-to-noise ratio, enabling the creation of higher-fidelity training datasets for robotic systems, improving robotic control models.
Implementation Method 1
The plurality of sensors mounted on the device may include at least one pressure sensor positioned on each of the plurality of finger elements to detect forces applied during object manipulation.
Implementation Method 2
Additionally, the wearable data collection device may include at least one position sensor at each of the plurality of joints configured to capture angle data.
Implementation Method 3
The plurality of sensors mounted on the device may include at least one camera mounted on the device to capture visual data from the perspective of the hand during manipulation tasks.
Implementation Method 4
In some embodiments, the wearable data collection device may include one or more time-of-flight (ToF) sensors to provide precise distance measurements to objects in the environment.
Implementation Method 5
The plurality of sensors mounted on the device may include at least one piezoelectric microphone mounted on the device to detect vibrations caused by contact between the wearable data collection device and objects.
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.