Robustness study of the bio-inspired musculoskeletal arm robot based on the data-driven iterative learning algorithm

2025-10-28

Science China Information Sciences

AI Summary

To address insufficient robustness of robotic dexterous manipulation in unstructured environments, this paper proposes LTDM-Arm, a biomimetic musculoskeletal robotic arm. Methodologically, it features: (1) a lightweight 7-DOF tendon-driven musculoskeletal architecture integrating 15 modular artificial muscle units; (2) a hybrid control framework combining Hill-type biomechanical muscle modeling with data-driven iterative learning control (DDILC) to achieve compliant, multi-DOF coordinated trajectory tracking; and (3) model-data joint modeling to enhance disturbance rejection. Experimental results demonstrate stable trajectory tracking under 15% load disturbances, while simulations tolerate up to 20% disturbances. The approach significantly improves motion accuracy, repeatability of learning, and environmental adaptability compared to conventional robotic arms.

Technology Category

• Intelligent Robots: Manipulation
• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Learning to Search

Application Category

• 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
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

The human arm exhibits remarkable capabilities, including both explosive power and precision, which demonstrate dexterity, compliance, and robustness in unstructured environments. Developing robotic systems that emulate humanlike operational characteristics through musculoskeletal structures has long been a research focus. In this study, we designed a novel lightweight tendon-driven musculoskeletal arm (LTDM-Arm), featuring a seven degree-of-freedom (DOF) skeletal joint system and a modularized artificial muscular system (MAMS) with 15 actuators. Additionally, we employed a Hilly-type muscle model and data-driven iterative learning control (DDILC) to learn and refine activation signals for repetitive tasks within a finite time frame. We validated the anti-interference capabilities of the musculoskeletal system through both simulations and experiments. The results show that the LTDM-Arm system can effectively achieve desired trajectory tracking tasks, even under load disturbances of 20% in simulation and 15% in experiments. This research lays the foundation for developing advanced robotic systems with human-like operational performance.

Problem

• Developing robotic systems that emulate human arm capabilities through musculoskeletal structures
• Learning activation signals for repetitive tasks using data-driven iterative learning control
• Validating anti-interference capabilities of musculoskeletal systems under load disturbances

Innovation

• Lightweight tendon-driven musculoskeletal arm design
• Data-driven iterative learning control algorithm
• Hilly-type muscle model for activation signals