Consistency-Driven Dual LSTM Models for Kinematic Control of a Wearable Soft Robotic Arm

TL;DR

This paper presents a dual LSTM framework with cycle consistency loss for accurate kinematic modeling of a wearable soft robotic arm, improving prediction accuracy and physical realism in complex, real-world tasks.

Contribution

Introduces a novel consistency-driven dual LSTM approach with cycle loss for better kinematic modeling of soft robots, addressing nonlinearities and hysteresis.

Findings

Cycle consistency loss improves prediction accuracy

Enhanced physical realism and stability in inverse kinematics

Successful deployment in human-robot collaboration tasks

Abstract

In this paper, we introduce a consistency-driven dual LSTM framework for accurately learning both the forward and inverse kinematics of a pneumatically actuated soft robotic arm integrated into a wearable device. This approach effectively captures the nonlinear and hysteretic behaviors of soft pneumatic actuators while addressing the one-to-many mapping challenge between actuation inputs and end-effector positions. By incorporating a cycle consistency loss, we enhance physical realism and improve the stability of inverse predictions. Extensive experiments-including trajectory tracking, ablation studies, and wearable demonstrations-confirm the effectiveness of our method. Results indicate that the inclusion of the consistency loss significantly boosts prediction accuracy and promotes physical consistency over conventional approaches. Moreover, the wearable soft robotic arm demonstrates strong human-robot collaboration capabilities and adaptability in everyday tasks such as object handover, obstacle-aware pick-and-place, and drawer operation. This work underscores the promising potential of learning-based kinematic models for human-centric, wearable robotic systems.