Event-Driven Visual-Tactile Sensing for Low-Latency Robot Perception
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Solution Overview
Problem
Current tactile sensing technology in robotics is limited by high latency and difficulty in scaling due to serial readout nature and complex integration with robot platforms, and event-driven perception systems for robots lack development compared to synchronous methods.
Innovation Solution
The NeuTouch event-driven tactile sensor and Visual-Tactile Spiking Neural Network (VT-SNN) system, which uses a neuromorphic design with asynchronous data transmission and combines vision and tactile modalities for efficient classification tasks, leveraging Spiking Neural Networks (SNNs) and neuromorphic hardware for low-latency and power-efficient processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional tactile sensors with serial readout are used, then integration with robot platforms is achieved, but latency is high and scaling is difficult
Solution Approach 1:
The patent replaces conventional serial readout mechanisms with an event-driven parallel architecture. Tactile sensors generate events asynchronously based on stimulus thresholds, eliminating the need for sequential scanning. This substitution of the readout mechanism fundamentally reduces latency while enabling straightforward parallel processing and scaling across multiple sensor elements.
Solution Approach 2:
The system transitions from static, periodic sampling to dynamic, event-triggered sampling. Sensors continuously monitor stimuli and generate events only when threshold conditions are met, adapting the readout rate to actual stimulus conditions. This dynamic approach reduces average latency and bandwidth usage while maintaining responsiveness to important tactile events.
2Measurement precision
If deep learning methods are used for visual-tactile perception, then classification accuracy is improved, but power consumption is high
Solution Approach 1:
The patent extracts and processes only the most salient features from visual and tactile streams using event-based processing. By focusing computational resources on significant events rather than processing all sensor data continuously, the system achieves high classification accuracy with reduced computational load and lower power consumption compared to full deep learning pipelines.
Solution Approach 2:
The system uses event-triggered periodic processing where computation is activated only when meaningful tactile or visual events occur. This intermittent processing pattern, rather than continuous deep learning inference, dramatically reduces average power consumption while maintaining accurate classification through timely processing of critical events.
3Productivity
If event-driven tactile sensors are implemented, then latency is reduced and scaling is enabled, but integration complexity increases
Solution Approach 1:
The patent employs a universal event-based interface that can accommodate multiple sensor types and configurations. The event-driven architecture serves as a common language for diverse tactile sensors, enabling scalable integration across different robot platforms without requiring platform-specific customizations. This universality reduces integration complexity despite the advanced processing capabilities.
Solution Approach 2:
The system introduces an event-based intermediary layer between physical sensors and processing algorithms. This intermediary translates various sensor outputs into a standardized event format, simplifying integration by decoupling sensor hardware from processing software. The intermediary handles timing, synchronization, and data format conversion, reducing overall system integration complexity.
Data Source
AI summary
A classifying sensing system, a classifying method performed using a sensing system, a tactile sensor, and a method of fabricating a tactile sensor. The classifying sensing system comprises a first spiking neural network, SNN, encoder configured for encoding an event-based output of a vision sensor into individual vision modality spiking representations with a first output size; a second SNN encoder configured for encoding an event-based output of a tactile sensor into individual tactile modality spiking representations with a second output size; a combination layer configured for merging the vision modality spiking representations and the tactile modality spiking representations; and a task SNN configured to receive the merged vision modality spiking representations and tactile modality spiking representations and output vision-tactile modality spiking representations with a third output size for classification.