Online Self-body Image Acquisition for Tendon-driven Musculoskeletal Humanoids Considering Muscle Route Changes Caused by Soft Body Tissue

Core Concepts

Tendon-driven musculoskeletal humanoids face challenges in controlling their complex body structures, including large differences between the actual robot and its geometric model, and movements that do not appear as changes in muscle lengths due to soft body tissue. This study proposes a method to acquire an accurate self-body image by constructing and updating two models: an ideal joint-muscle model and a muscle-route change model, using sensor information from the actual robot.

Abstract

The study addresses the challenges faced by tendon-driven musculoskeletal humanoids in controlling their complex body structures. The key points are:

Initialization of self-body image using a man-made geometric model:

Ideal Joint-Muscle Model (IJMM) is constructed to express the relationship between joint angles and muscle lengths.
Muscle-Route Change Model (MRCM) is constructed to compensate for muscle route changes caused by soft body tissue.

Online learning of self-body image using sensor information from the actual robot:

Antagonism Updater updates the antagonism of IJMM to address the large difference between the actual robot and its geometric model.
Vision Updater updates IJMM and MRCM based on information from the vision sensor.

Experiments demonstrate the effectiveness of the proposed system:

Online learning improves the accuracy of joint angle estimation, especially when external forces are applied.
The learned self-body image enables the robot to maintain the target posture when grasping a heavy object, compensating for muscle route changes.

The study presents a comprehensive approach to address the challenges of tendon-driven musculoskeletal humanoids by constructing and updating self-body image models, enabling the robots to move as intended and perceive their self-movements accurately.

Stats

The muscle elongation depends on the muscle tension T and the absolute muscle length labs, as shown in the following equation:
T = k∆l/labs
The compensation value of muscle length, ∆lcomp, is calculated as:
∆lcomp = -(αT + βlT) = -(αT + βfgeo,abs(θ)T)
where α is the coefficient for muscle wire elongation, β is the coefficient for structure deformation, and fgeo,abs is the function to calculate the absolute muscle lengths from the geometric model.

Quotes

"Tendon-driven musculoskeletal humanoids have many complex structures, such as multi-DOF joint structure of the spine and redundant muscles covering joints, and it is very difficult to modelize in detail."
"There are movements which the muscle length sensors cannot measure, due to changes in muscle routes caused by softness of body tissue."

Key Insights Distilled From

Online Self-body Image Acquisition Considering Changes in Muscle Routes Caused by Softness of Body Tissue for Tendon-driven Musculoskeletal Humanoids

by Kento Kawaha... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05286.pdf

Deeper Inquiries

How can the online learning process be further optimized to reduce the time required for the self-body image to converge

To optimize the online learning process and reduce the convergence time of the self-body image, several strategies can be implemented.

Batch Learning: Instead of updating the models with every new data point, batch learning can be employed. This involves accumulating a set of data points before updating the models, reducing the frequency of updates and potentially improving the stability of the learning process.

Improved Initialization: Enhancing the initialization of the models can help accelerate convergence. By starting closer to the optimal solution, the models may require fewer updates to reach an accurate representation of the self-body image.

Dynamic Learning Rate: Implementing a dynamic learning rate that adjusts based on the rate of convergence can speed up the learning process. Initially using a higher learning rate for rapid progress and then decreasing it as the models approach convergence can be an effective strategy.

Parallel Processing: Utilizing parallel processing capabilities can allow for simultaneous updates of different parts of the model, reducing the overall time required for convergence.

Transfer Learning: Leveraging knowledge from previous learning tasks or similar domains can provide a head start in the learning process, enabling quicker adaptation to the specific requirements of the self-body image acquisition.

What are the potential limitations or drawbacks of the proposed approach, and how could they be addressed

While the proposed approach shows promise in addressing the challenges of controllability in tendon-driven musculoskeletal humanoids, there are potential limitations and drawbacks that need to be considered:

Complexity of Models: The use of neural networks for modeling can introduce complexity and potential overfitting. Regularization techniques and careful monitoring of model performance are essential to mitigate these risks.

Sensor Noise and Variability: Real-world sensor data may contain noise and variability, impacting the accuracy of the self-body image acquisition. Robust techniques for handling sensor noise and uncertainty need to be integrated into the learning process.

Generalization: The models developed in this study may be specific to the characteristics of tendon-driven musculoskeletal humanoids. Ensuring the generalizability of the self-body image acquisition techniques to different robotic systems requires further validation and adaptation.

Computational Resources: The online learning process may require significant computational resources, especially when dealing with large datasets or complex models. Optimization of algorithms and efficient utilization of hardware can help mitigate this limitation.

Addressing these limitations could involve incorporating additional regularization techniques, enhancing sensor data preprocessing methods, conducting extensive validation on diverse robotic platforms, and optimizing algorithms for scalability and efficiency.

How could the self-body image acquisition techniques developed in this study be applied to other types of robotic systems beyond tendon-driven musculoskeletal humanoids

The self-body image acquisition techniques developed for tendon-driven musculoskeletal humanoids can be applied to a wide range of robotic systems beyond this specific domain.

Industrial Robots: The principles of self-body image acquisition can be utilized in industrial robots to enhance their controllability and adaptability in complex manufacturing environments. By incorporating similar models and online learning processes, industrial robots can improve their performance and efficiency.

Rehabilitation Robots: Robotic systems used in rehabilitation settings can benefit from self-body image acquisition techniques to better understand and respond to the movements and needs of patients. This can lead to more personalized and effective rehabilitation programs.

Assistive Robots: Robots designed to assist individuals with daily tasks or mobility challenges can leverage self-body image acquisition to enhance their interaction capabilities and adapt to different environments. This can improve the autonomy and usability of assistive robotic systems.

Exploration Robots: Robotic systems used in exploration missions, such as space or deep-sea exploration, can utilize self-body image acquisition to adapt to changing conditions and navigate complex terrains more effectively. This can enhance the autonomy and robustness of exploration robots.

By applying the self-body image acquisition techniques to diverse robotic systems, the capabilities and performance of these robots can be significantly enhanced, leading to more versatile and adaptive robotic platforms.