用双LSTM模型精准控制可穿戴软机械臂，提升运动预测稳定性。
Consistency-Driven Dual LSTM Models for Kinematic Control of a Wearable Soft Robotic Arm
- 采用双LSTM结构联合学习正逆运动学，捕捉气动软体的非线性与滞后特性。
- 引入循环一致性损失，使逆向预测准确率显著提升，物理合理性更强。
- 适用于日常任务协作，如递物、避障抓取和开抽屉，适合人机协同场景。
本文提出一种一致性驱动的双LSTM框架，用于精确学习集成于可穿戴设备中的气动软体机械臂的正逆运动学。该方法有效捕捉软气动执行器的非线性和滞回特性，并解决执行输入与末端位置之间的一对多映射难题。通过引入循环一致性损失，增强了物理真实性，提升了逆向预测的稳定性。大量实验——包括轨迹跟踪、消融研究和可穿戴演示——验证了该方法的有效性。结果表明，一致性损失的引入显著提高了预测精度，并在传统方法基础上增强了物理一致性。此外，可穿戴软体机械臂在物体传递、避障抓取和抽屉操作等日常任务中展现出强人机协作能力与环境适应性。本工作凸显了基于学习的运动学模型在面向人类的可穿戴机器人系统中的巨大潜力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。