Unsupervised Sim-to-Real Adaptation of Soft Robot Proprioception Using a Dual Cross-Modal Autoencoder

Data-driven calibration methods have shown promising results for accurate proprioception in soft robotics. This process can be greatly benefited by adopting numerical simulation for computational efficiency. However, the gap between the simulated and real domains limits the accurate, generalized app...

Full description

Saved in:
Bibliographic Details
Published in:Soft robotics Vol. 12; no. 2; p. 213
Main Authors: Park, Chaeree, Park, Hyunkyu, Kim, Jung
Format: Journal Article
Language:English
Published: United States 01.04.2025
Subjects:
collision detection
soft sensors
soft robotics
domain adaptation
proprioception
ISSN:2169-5180, 2169-5180
Online Access:Get more information
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Data-driven calibration methods have shown promising results for accurate proprioception in soft robotics. This process can be greatly benefited by adopting numerical simulation for computational efficiency. However, the gap between the simulated and real domains limits the accurate, generalized application of the approach. Herein, we propose an unsupervised domain adaptation framework as a data-efficient, generalized alignment of these heterogeneous sensor domains. A dual cross-modal autoencoder was designed to match the sensor domains at a feature level without any extensive labeling process, facilitating the computationally efficient transferability to various tasks. Moreover, our framework integrates domain adaptation with anomaly detection, which endows robots with the capability for external collision detection. As a proof-of-concept, the methodology was adopted for the famous soft robot design, a multigait soft robot, and two fundamental perception tasks for autonomous robot operation, involving high-fidelity shape estimation and collision detection. The resulting perception demonstrates the digital-twinned calibration process in both the simulated and real domains. The proposed design outperforms the existing prevalent benchmarks for both perception tasks. This unsupervised framework envisions a new approach to imparting embodied intelligence to soft robotic systems via blending simulation.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:2169-5180
2169-5180
DOI:10.1089/soro.2024.0025