Robot Haptic Interaction Control With Sparse Tactile Sensing
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Solution Overview
Problem
Current human-robot interaction systems face challenges in efficiently processing high-dimensional, sparse sensor data from continuous-time tactile information, leading to increased computational complexity and reduced performance in whole-body haptic interactions.
Innovation Solution
The system employs a processor that reduces sensor data dimensionality using temporal and spatial sparsity techniques, such as masking data during non-interaction periods and mutual information-based feature selection, and generates responses based on a Bayesian Interaction Primitives model approximated with a Monte Carlo Ensemble, enabling effective whole-body haptic interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous-time tactile information from multiple sensors is processed in detail, then measurement precision and interaction detection capability are improved, but computational complexity increases significantly
Solution Approach 1:
The patent extracts and processes only the most relevant features from high-dimensional sensor data. The system identifies key interaction parameters (force magnitude, contact location, duration) and extracts these specific features while discarding redundant information, thereby reducing computational complexity while maintaining measurement precision for critical interaction parameters.
Solution Approach 2:
The patent transforms continuous-time sensor signals into discrete event representations by changing the parameter representation. Instead of processing continuous force signals from all sensors simultaneously, the system detects threshold crossings and converts them into discrete interaction events with key parameters (start time, end time, peak force), significantly reducing computational load while preserving essential measurement information.
2Measurement precision
If high-dimensional sensor data from all sensors is processed continuously, then interaction detection accuracy is improved, but processing time increases
Solution Approach 1:
The patent implements periodic sampling and event-triggered processing rather than continuous processing of all sensor data. The system periodically checks sensor readings and triggers detailed processing only when interaction events are detected (when force thresholds are crossed), allowing rapid response to interactions while reducing overall processing time during non-interaction periods.
Solution Approach 2:
The patent segments the sensor data processing into independent interaction events. Each detected interaction is processed as a separate event with its own parameter extraction and response generation, allowing parallel processing of multiple simultaneous interactions and reducing the time required to process high-dimensional sensor data from all sensors.
3Loss of information
If data from all sensors is retained and processed, then information completeness is improved, but data sparsity and computational burden increase
Solution Approach 1:
The patent merges data from multiple sensors that detect the same interaction event. Instead of processing each sensor independently, the system combines force data from multiple force sensors, motion data from IMUs, and tactile data from contact sensors into a unified interaction representation, reducing computational burden while maintaining information completeness about the overall interaction.
Solution Approach 2:
The patent processes sensor data at different levels of detail based on interaction context. During clear interaction events, full sensor data is processed for complete information. During ambiguous periods, only critical sensors are processed. This partial processing approach reduces overall computational burden while maintaining information completeness when needed most.
Data Source
AI summary
A robot for physical human-robot interaction may include a number of sensors, a processor, a controller, an actuator, and a joint. The sensors may receive a corresponding number of sensor measurements. The processor may reduce a dimensionality of the number of sensor measurements based on temporal sparsity associated with the number of sensors and spatial sparsity associated with the number of sensors and generate an updated sensor measurement dataset. The processor may receive an action associated with a human involved in pHRI with the robot. The processor may generate a response for the robot based on the updated sensor measurement dataset and the action. The controller may implement the response via an actuator within a joint of the robot.