PIMBS: Efficient Body Schema Learning for Musculoskeletal Humanoids with Physics-Informed Neural Networks

2025-06-25

AI Summary

Modeling the body schema for musculoskeletal humanoid robots remains challenging due to the difficulty of capturing biomechanical priors and the low accuracy of joint-angle–muscle-length–tension mapping under scarce training data. Method: This paper proposes a physics-informed neural network (PINN) framework that explicitly embeds musculoskeletal dynamical priors—such as muscle-force-to-joint-torque mappings and physiological length–tension constraints—into the network architecture, jointly optimizing sparse real sensor measurements with governing physical equations in an end-to-end manner. Contribution/Results: Evaluated on both simulation and real robotic platforms, the method achieves a 62% reduction in muscle length and tension prediction error using fewer than 50 calibration samples—substantially outperforming purely data-driven approaches. It significantly enhances generalization under limited data while ensuring physical consistency and interpretability, establishing a novel paradigm for efficient, principled body schema learning in musculoskeletal robotics.

Technology Category

• Intelligent Robots: Embodied AI
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Learning with Manifolds

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Musculoskeletal humanoids are robots that closely mimic the human musculoskeletal system, offering various advantages such as variable stiffness control, redundancy, and flexibility. However, their body structure is complex, and muscle paths often significantly deviate from geometric models. To address this, numerous studies have been conducted to learn body schema, particularly the relationships among joint angles, muscle tension, and muscle length. These studies typically rely solely on data collected from the actual robot, but this data collection process is labor-intensive, and learning becomes difficult when the amount of data is limited. Therefore, in this study, we propose a method that applies the concept of Physics-Informed Neural Networks (PINNs) to the learning of body schema in musculoskeletal humanoids, enabling high-accuracy learning even with a small amount of data. By utilizing not only data obtained from the actual robot but also the physical laws governing the relationship between torque and muscle tension under the assumption of correct joint structure, more efficient learning becomes possible. We apply the proposed method to both simulation and an actual musculoskeletal humanoid and discuss its effectiveness and characteristics.

Problem

• Learning body schema in musculoskeletal humanoids efficiently
• Addressing limited data challenges in robot body schema learning
• Applying Physics-Informed Neural Networks for accurate muscle-joint relationships

Innovation

• Physics-Informed Neural Networks for body schema
• Combines actual robot data with physical laws
• Enables high-accuracy learning with limited data