MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control (2205.13600v1)

Abstract: Embodied agents in continuous control domains have had limited exposure to tasks allowing to explore musculoskeletal properties that enable agile and nimble behaviors in biological beings. The sophistication behind neuro-musculoskeletal control can pose new challenges for the motor learning community. At the same time, agents solving complex neural control problems allow impact in fields such as neuro-rehabilitation, as well as collaborative-robotics. Human biomechanics underlies complex multi-joint-multi-actuator musculoskeletal systems. The sensory-motor system relies on a range of sensory-contact rich and proprioceptive inputs that define and condition muscle actuation required to exhibit intelligent behaviors in the physical world. Current frameworks for musculoskeletal control do not support physiological sophistication of the musculoskeletal systems along with physical world interaction capabilities. In addition, they are neither embedded in complex and skillful motor tasks nor are computationally effective and scalable to study large-scale learning paradigms. Here, we present MyoSuite -- a suite of physiologically accurate biomechanical models of elbow, wrist, and hand, with physical contact capabilities, which allow learning of complex and skillful contact-rich real-world tasks. We provide diverse motor-control challenges: from simple postural control to skilled hand-object interactions such as turning a key, twirling a pen, rotating two balls in one hand, etc. By supporting physiological alterations in musculoskeletal geometry (tendon transfer), assistive devices (exoskeleton assistance), and muscle contraction dynamics (muscle fatigue, sarcopenia), we present real-life tasks with temporal changes, thereby exposing realistic non-stationary conditions in our tasks which most continuous control benchmarks lack.

• The paper introduces an automated, model-agnostic pipeline that accelerates musculoskeletal simulations by up to 100 times compared to traditional methods.
• It demonstrates nine diverse manipulation tasks, including pen twirling and Baoding ball rotation, to challenge current AI algorithms with realistic dynamics.
• The framework simulates non-stationary conditions like muscle fatigue and sarcopenia, providing insights for rehabilitation and collaborative robotics research.

MyoSuite: A Comprehensive Simulation Framework for Musculoskeletal Motor Control

The paper introduces MyoSuite, an advanced simulation suite designed to address the intricacies of musculoskeletal motor control. This framework provides a robust platform for modeling physiologically accurate biomechanical systems, focusing on the human elbow, wrist, and hand. By incorporating contact-rich interactions, MyoSuite facilitates the paper of complex, skillful tasks.

Key Contributions

The authors present several noteworthy contributions:

1. Automated Model-Agnostic Pipeline: The suite introduces an automated pipeline that streamlines the creation of validated musculoskeletal models from pre-existing OpenSim models. This approach significantly reduces manual effort and allows simulations to run two orders of magnitude faster.
2. Comprehensive Task Design: A range of nine manipulation tasks are crafted, varying from simple configurations to intricate manipulations like pen twirling and Baoding ball rotation. These tasks are inspired by state-of-the-art robotic manipulation achievements and challenge current AI algorithms with realistic dynamics.
3. Non-Stationary Conditions: MyoSuite includes simulations of real-life physiological changes, such as muscle fatigue and sarcopenia, further challenging algorithmic adaptability. Such non-stationary environments mirror realistic scenarios often overlooked in traditional models.
4. Simulation Performance: The integration of these models within the MuJoCo physics engine results in simulations that are not only more physiologically realistic but also computationally efficient, providing a significant speed advantage over traditional platforms.

Experimental Results

The paper validates the performance of the conversion pipeline through thorough testing with existing OpenSim models. Validation results demonstrate close alignment of muscle force and moment arm characteristics between the original and converted models, ensuring anatomical and dynamic fidelity. Comparative performance analysis shows that MyoSuite can handle complex models efficiently, far surpassing OpenSim in computational speed.

Baseline experiments using the Natural Policy Gradient (NPG) method indicate variable success rates across tasks. The experiments also showcase the suite's potential for revealing adaptive muscle behaviors under conditions like muscle sarcopenia and fatigue. Particularly, the paper of tendon transfer surgeries highlights the suite's capability to simulate and evaluate neuromuscular adaptations post-injury, which could guide therapeutic practices.

Implications and Future Directions

The introduction of MyoSuite offers significant implications for AI, biomechanics, and robotics:

• AI Algorithms: MyoSuite presents unique continuous control challenges with third-order dynamics, potentially driving advancements in AI algorithms geared towards adaptive and efficient motor control.
• Biomechanics and Rehabilitation: The suite enables realistic simulation of musculoskeletal interactions, potentially aiding the development of better rehabilitation protocols and assistive technologies.
• Collaborative Robotics: By modeling human-muscle dynamics accurately, the toolkit can be instrumental in the co-design of robots that interact seamlessly with humans.

Future enhancements could explore expanding the suite to include more complex musculoskeletal systems or incorporate real-time data integration for more dynamic simulations. Additionally, cross-disciplinary utilization of MyoSuite may uncover new insights into motor control strategies and rehabilitation methodologies.

In summary, MyoSuite addresses existing gaps in musculoskeletal modeling and presents a platform poised to catalyze innovations across several research domains. Its blend of physiological accuracy and computational efficiency sets a new benchmark for studying the complexities of musculoskeletal motor control.