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

2026-03-18

AI Summary

This work addresses the challenges of nonlinearities, hysteresis, and one-to-many input–output mappings inherent in the forward and inverse kinematic modeling of pneumatic-driven wearable soft robotic arms. To overcome these issues, the authors propose a consistency-driven dual-LSTM framework that jointly learns forward and inverse kinematic models while enforcing mutual constraints through a cycle-consistency loss during training. This approach significantly enhances the physical plausibility and stability of inverse predictions. Experimental results demonstrate that the proposed method outperforms conventional approaches across multiple tasks—including trajectory tracking, ablation studies, and real-world wearable scenarios such as object handover, obstacle-avoidance grasping, and drawer manipulation—achieving notable improvements in accuracy, robustness, and human–robot collaboration performance.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• soft robotic arm
• kinematic control
• inverse kinematics
• hysteresis
• one-to-many mapping

Innovation

• consistency-driven learning
• dual LSTM
• soft robotic arm
• inverse kinematics
• cycle consistency loss