Unsupervised Sim-to-Real Adaptation for Environmental Recognition in Assistive Walking.

Area of Science:

• Robotics
• Artificial Intelligence
• Biomedical Engineering

Background:

• Powered lower-limb prostheses utilize vision sensors for environmental recognition to restore amputee mobility.
• The sim-to-real gap, caused by differences in real-world terrains and sensor limitations, hinders supervised learning for prosthetic adaptation.
• Existing methods struggle to achieve accurate terrain classification in diverse, unstructured environments.

Purpose of the Study:

• To develop an unsupervised sim-to-real adaptation method for accurate environmental classification in powered lower-limb prostheses.
• To enhance terrain-adaptive locomotion for amputees by overcoming limitations of simulated data.
• To enable robust performance in real-world conditions without requiring labeled real-world data.

Main Methods:

• Augmented simulated environments were generated from a virtual camera perspective to mimic real-world conditions.
• Unsupervised domain adaptation was employed, training a network on simulated and unlabeled real-world data to minimize domain shift.
• The adaptation network, comprising a feature extractor and two classifiers, was trained to classify five common terrains: level ground, stair ascent/descent, and ramp ascent/descent.

Main Results:

• The unsupervised method achieved high average accuracies of 98.06% (indoor) and 95.91% (outdoor) across eight subjects.
• These results closely approach the performance of supervised learning methods that use labeled real-world data (98.37% indoor, 97.05% outdoor).
• Visualizations demonstrated the network's ability to extract and interpret essential terrain features.

Conclusions:

• The proposed unsupervised sim-to-real adaptation method effectively bridges the reality gap for prosthetic environmental classification.
• This approach enables accurate real-world terrain recognition and successful sim-to-real transfer, crucial for adaptive prosthetic locomotion.
• The findings show significant potential for improving prosthetic functionality and amputee mobility in complex environments.