Adaptive Body Schema Learning System Considering Additional Muscles for Musculoskeletal Humanoids (2411.06322v1)

Abstract: One of the important advantages of musculoskeletal humanoids is that the muscle arrangement can be easily changed and the number of muscles can be increased according to the situation. In this study, we describe an overall system of muscle addition for musculoskeletal humanoids and the adaptive body schema learning while taking into account the additional muscles. For hardware, we describe a modular body design that can be fitted with additional muscles, and for software, we describe a method that can learn the changes in body schema associated with additional muscles from a small amount of motion data. We apply our method to a simple 1-DOF tendon-driven robot simulation and the arm of the musculoskeletal humanoid Musashi, and show the effectiveness of muscle tension relaxation by adding muscles for a high-load task.

• The paper presents an adaptive system that updates the musculoskeletal body schema using minimal motion data to incorporate additional muscles.
• It employs a modular hardware design integrating actuators and sensors to facilitate reliable, task-specific muscle additions.
• The system’s relearning strategy balances new and existing data, reducing control error and enhancing performance in humanoid robotics.

Adaptive Body Schema Learning System for Musculoskeletal Humanoids

The paper presents an advanced system for musculoskeletal humanoids that leverages the inherent advantages of modularity and adaptability in muscle arrangement. The authors explicitly focus on adaptive body schema learning that facilitates muscle addition, allowing for task-specific modifications that enhance humanoid functionality. This capability is distinct in musculoskeletal systems as opposed to axis-driven robots, providing a unique avenue for research and development in humanoid robotics.

Key Contributions

1. Modular Hardware Design: The system makes substantial use of muscle modules that integrate critical components such as actuators, motor drivers, and tension measurement units. This modular design significantly boosts reliability and simplifies the process of muscle addition, allowing customization based on task requirements.
2. Adaptive Body Schema Learning: The research introduces a robust framework for relearning the musculoskeletal body schema whenever muscles are added. By acquiring minimal motion data post-modification, the system efficiently updates its body schema model to incorporate changes, ensuring seamless transition and utilization of new muscular configurations.
3. Improved Learning Techniques: The paper demonstrates multiple relearning strategies, notably involving a weighted balance between old data retention and new data integration. This approach reduces the potential for overfitting to the new data and mitigates the forgetting of pre-existing learned configurations.

Detailed System Implementation and Evaluation

The researchers implemented their system using the Musculoskeletal AutoEncoder (MAE), an efficient neural network model that encapsulates the static intersensory relationships in musculoskeletal systems. MAE's adaptability allows it to represent changes succinctly across various interaction scenarios, as demonstrated in the simulations on a 1-DOF tendon-driven robot and practical implementations on the Musashi humanoid's arm.

Experimental results provided quantitative validation of their adaptive system's capacity to maintain high control accuracy following structural modifications. In tasks involving significant muscular reconfiguration, the system demonstrated reduced control error and efficient load distribution among muscles, showcasing the benefits of adaptive relearning.

Implications and Opportunities

The emerging capability to dynamically add muscles based on tasks greatly enhances the potential applications of these humanoids in varied and complex environments. The inclusion of adaptive learning methodologies allows the humanoid to maintain performance across different operational contexts and adapt tactically to alterations in physical structure.

The system architecture proposed in this paper provides a reliable foundation for further advancements in humanoid adaptability, suggesting pathways towards more autonomous, resilient, and versatile robotic systems. Future research could explore expanding the applications of this technology, extending to domains requiring robust adaptability, such as field robotics and interactive service robots.

The core contributions of the paper align well with ongoing efforts to enhance humanoid robots' efficiency and versatility, making it a valuable contribution to the field of robotic research and development. By addressing the pressing need for adaptability and efficiency beyond static configurations, this work underscores the relevance of flexible and responsive robotic systems tailored for dynamic real-world applications.