Robust Motion Learning for Musculoskeletal Robots Based on a Recurrent Neural Network and Muscle Synergies

Musculoskeletal robots with human-like joints, muscles, and actuation mechanisms are characterized by exceptional dexterity, compliance, and versatility. However, existing reinforcement learning methods for such robots rely on precise and sufficient state observation, rendering them vulnerable to perturbations. To address this limitation, this paper proposes a robust motion learning method based on a recurrent neural network (RNN) and muscle synergy. First, the proposed method utilizes task-joint-muscle space states to create an RNN-based neuromuscular controller. Furthermore, a motion learning method with a synergistic constraint of muscles is developed. Additionally, theoretical analysis confirms that the RNN-based controller is more robust to perturbations of state observation than a Multilayer Perceptron (MLP) based controller. The proposed method is evaluated on a simulated musculoskeletal robot and demonstrates superior robustness to other MLP-based reinforcement learning methods. Furthermore, the proposed method is also validated on a musculoskeletal robot hardware system, indicating its potential for real-world applications. <italic>Note to Practitioners</italic>&#x2014;Musculoskeletal robots have shown promising potential in various applications, but their development and application have been restricted by the limited robustness of existing reinforcement learning methods. These methods work well in simulation with precise and sufficient state observation, but exhibit a considerable degradation of performance in real-world environments with perturbed and insufficient state observation. In this article, we propose a novel method to improve the robustness of motion learning for musculoskeletal robots using a recurrent neural network and muscle synergy. The proposed method is theoretically and experimentally validated to perform well not only under precise and sufficient state observation but also in the presence of perturbed and insufficient state observation. Our results demonstrate the effectiveness of the proposed method and motivate the application of reinforcement learning methods to musculoskeletal robots. This research contributes to the advancement of robust motion learning for musculoskeletal robots and paves the way for wider adoption in real-world applications.