Robust Continuous Motion Strategy Against Muscle Rupture using Online Learning of Redundant Intersensory Networks for Musculoskeletal Humanoids (2409.14951v1)

Abstract: Musculoskeletal humanoids have various biomimetic advantages, of which redundant muscle arrangement is one of the most important features. This feature enables variable stiffness control and allows the robot to keep moving its joints even if one of the redundant muscles breaks, but this has been rarely explored. In this study, we construct a neural network that represents the relationship among sensors in the flexible and difficult-to-modelize body of the musculoskeletal humanoid, and by learning this neural network, accurate motions can be achieved. In order to take advantage of the redundancy of muscles, we discuss the use of this network for muscle rupture detection, online update of the intersensory relationship considering the muscle rupture, and body control and state estimation using the muscle rupture information. This study explains a method of constructing a musculoskeletal humanoid that continues to move and perform tasks robustly even when one muscle breaks.

• The paper presents RMAE, an autoencoder that leverages redundant sensor data to predict missing information after muscle rupture.
• It employs online learning to refine state estimation and control accuracy, significantly reducing joint angle errors in simulations and experiments.
• The study underscores the importance of anomaly detection and real-time adaptation for developing resilient, fail-safe humanoid robotics.

Robust Continuous Motion Strategy for Musculoskeletal Humanoids

The paper presents a novel control strategy for musculoskeletal humanoids that can maintain functionality despite muscle rupture. The work revolves around utilizing the redundancy inherent in musculoskeletal designs. These robots are modeled to mimic human anatomy, providing advantages like variable stiffness and fail-safe joint operation even if a muscle fails. This research introduces the Redundant Musculoskeletal AutoEncoder (RMAE), a neural network architecture that encapsulates the interdependencies between joint angles, muscle tension, and muscle length.

Methodology

The core innovation of this paper is the creation and application of RMAE. It is designed to harness the redundancy in sensor networks by learning and adapting to changes in the robot's physical state. This allows for continuous joint operation, even after a muscle rupture. The autoencoder predicts the missing sensor information when partial data is available due to sensor failure. RMAE undergoes initial training to establish a baseline model of the musculoskeletal structure and is then refined through online learning using live data.

The paper further defines a robust mechanism for detecting anomalies and verifying muscle rupture. It leverages the prediction discrepancies in RMAE to detect potential muscle failures, and through a systematic verification procedure, it distinguishes between actual muscle ruptures and sensor noise or fault.

Experimental Setup and Results

The research utilizes both simulations and physical experiments with the humanoid robot Musashi to validate the methodology. In a controlled simulation involving a single degree of freedom (DOF), the RMAE significantly reduced joint angle estimation errors through online learning. The paper examined scenarios where a muscle was fractured, and highlighted how the presence of muscle rupture information ($\bm{r}$) improved the state estimation accuracy substantially, especially when using the more computationally intense Method A' for refining the latent state.

In the actual robot experiments involving Musashi's upper limb, the paper showed that while online learning enhanced state estimation and control accuracy, the gap in performance between simulations and the actual robot is evident. It emphasizes the challenges in accurately modeling and controlling the complex and flexible anatomy of humanoid robots. The research further illustrates the importance of informing the control system of muscle rupture to prevent deterioration of performance.

Implications and Future Work

The implications of this research are significant for the development of resilient humanoid robots that can adapt to internal failures, potentially increasing their lifespan and reliability. The approach emphasizes a shift towards redundancy-aware control strategies that can dynamically alter their behavior based on sensed physical changes.

For future developments, the paper suggests extending the current framework to manage longer operational periods seamlessly, thereby creating a more robust robotic system capable of functioning continuously despite internal adversities. Moreover, optimizing muscle arrangements to accommodate any ruptures remains a priority, thus broadening the applicability of the strategy across diverse robotic systems. This work lays the groundwork for a more resilient category of robotics, sparking considerations for both hardware upgrades and more intelligent software frameworks.