Contrastive Autoencoder for Robust State Modelling of Soft Robots in Incomplete and Noisy Environments

Abstract

Soft robotic systems heavily depend on accurate sensor data for perception and control; however, this data is often corrupted by missing observations, due to partial sensor coverage, communication failures, or occlusions and noisy measurements stemming from hardware imperfections, environmental disturbances, and the intrinsic compliance of soft materials. Such corruption can obscure critical state information, causing unreliable modeling of soft robotics and degrading control accuracy. To address these challenges, we propose a Contrastive Dual-Latent Autoencoder (CDLAE) that jointly handles missing and noisy data in a single end-to-end framework. Our approach leverages an attention based autoencoder architecture with dual latent pathways, where one focuses on capturing the underlying clean signals while the other isolates noise-related components. A contrastive loss encourages strong separation between these pathways, enhancing the model’s ability to filter noise while reconstructing missing values. Additionally, the autoencoder is trained jointly with a downstream predictive network, ensuring that signal imputation is optimized with respect to the ultimate control task. Experimental evaluations on a pneumatic soft robot platform and multiple public time-series datasets demonstrate that CDLAE consistently outperforms existing methods in handling corrupted data, offering robust, high-fidelity reconstructions that significantly improve soft robot perception and control in real-world conditions.