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Learning to Predict Perceptual Distributions of Haptic Adjectives
Benjamin A Richardson1, Katherine J Kuchenbecker1
1Haptic Intelligence Department, Max Planck Institute for Intelligent Systems, Stuttgart, Germany.
Frontiers in Neurorobotics
|March 3, 2020
Summary
This study introduces a machine learning method enabling robots to perceive tactile properties like hardness and roughness, accounting for human perception variability. The approach models both the intensity and variation in haptic perception for improved robotic intelligence.
Area of Science:
- Robotics
- Artificial Intelligence
- Human-Computer Interaction
Background:
- Human tactile perception is subjective and variable, posing challenges for robotic systems.
- Previous robotic haptic intelligence research focused on binary attributes, neglecting intensity and variability.
Purpose of the Study:
- To develop a machine learning method for robots to predict ordinal haptic adjective distributions.
- To incorporate human perceptual variability and attribute intensity into robotic tactile sensing.
Main Methods:
- Collected ordinal haptic adjective labels from human subjects for 60 objects.
- Extracted features from multi-modal tactile data gathered by a robot.
- Designed a machine learning model integrating partial label distribution knowledge for training.
Main Results:
- The developed method predicts a probability distribution over ordinal haptic labels from a single interaction.
- Demonstrated the model's ability to capture both intensity and variation in haptic perception.
- Analyzed the influence of individual sensor modalities on adjective prediction.
Conclusions:
- The study successfully models intensity and variation in haptic perception, crucial for human-like robotic tactile intelligence.
- This work advances robotic capabilities in understanding and replicating nuanced tactile experiences.
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