Source-linked AI summary
MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control
Vittorio Caggiano, Huawei Wang, Guillaume Durandau, Massimo Sartori, Vikash Kumar
TL;DR
Embodied-control benchmarks have offered limited exposure to physiologically sophisticated, contact-rich, and non-stationary musculoskeletal behavior. MyoSuite provides efficient biomechanical models and diverse manipulation tasks with physiological alterations, while initial policies exhibit adaptive muscle coordination; the models and task variations remain an initial approximation requiring further validation.
Problem
Existing embodied-control frameworks lack physiological sophistication, complex contact-rich motor tasks, and computationally effective scalability for large-scale musculoskeletal learning.
Method
MyoSuite combines physiologically realistic MuJoCo musculoskeletal models with contact-rich tasks and variations including tendon transfer, fatigue, sarcopenia, and exoskeleton assistance.
Results
Basic policies exhibit physiologically relevant adaptation, including antagonistic muscle effects and co-contractions that compensate for sarcopenia and fatigue.
Takeaways & Limitations
MyoSuite offers realistic, complex benchmarks for studying motor control across machine learning, biomechanics, and robotics communities.
Takeaways & Limitations
The implemented models are a first approximation requiring further development and validation, and the tasks and physiological changes cover only a subset of possible variations.
Abstract
from arXiv · showhide
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.
1 Introduction
MyoSuite addresses the need for realistic, scalable embodied-control benchmarks by combining physiologically accurate musculoskeletal models, contact dynamics, dexterous tasks, and realistic non-stationarities.
- Motivation: Existing embodied-AI benchmarks largely use simple, mostly solved problems and provide limited tests of adaptation to changing environments.The paper identifies a need for benchmarks embedded more closely in real-world problems.
- Framework: MyoSuite provides physiologically realistic and computationally efficient musculoskeletal models in the MuJoCo physics engine.The framework is presented as a new platform for studying musculoskeletal control.
- Framework: The model pipeline preserves numerical equivalence to validated OpenSim models while enabling simulation two orders of magnitude faster.The pipeline automates model creation by optimizing existing validated models.
- Framework: MyoSuite includes physiologically accurate elbow, wrist, and hand models with full contact dynamics.The models range from simple one-joint systems to complex full-hand systems.
- Tasks: The suite offers 9 realistic dexterous manipulation task families, ranging from simple posing to simultaneous manipulation of two Baoding balls.The task families are designed around increasingly skillful manipulation behaviors.
- Non-stationarity: Physiological alterations include tendon transfer, exoskeleton assistance, muscle fatigue, and sarcopenia, exposing agents to temporal changes during continuous control.These alterations are intended to represent realistic non-stationary task conditions.
2 Preliminaries
The paper formulates musculoskeletal behavior synthesis as reinforcement learning over biomechanical systems whose muscle, tendon, and body dynamics create coupled continuous-control problems.
- Musculoskeletal models: Musculoskeletal models are commonly represented as third-order systems combining muscle activation, contractile dynamics, and second-order body dynamics.This structure distinguishes musculoskeletal control from simpler control formulations.
- Musculoskeletal models: A Hill-type muscle-tendon model represents contractile fibers, parallel elastic fiber stiffness, and series elastic tendon stiffness.Muscle force depends on muscle length and velocity through force-length and force-velocity properties.
- Musculoskeletal models: The musculoskeletal equations map muscle-tendon forces and moment arms to skeletal motion while incorporating activation, velocity, gravity, and Coriolis terms.The formulation connects muscle-level actuation to joint-level dynamics.
- Markov decision process formulation: Reinforcement learning is formulated as an MDP with continuous state and action spaces, transition dynamics, rewards, discounting, and an initial-state distribution.Policies map states to action distributions and are optimized for expected discounted return.
- Markov decision process formulation: For robotic systems, states contain joint positions and velocities, whereas musculoskeletal states additionally contain muscle-tendon lengths, velocities, and activations.Musculoskeletal actions are alpha-motoneuron signals rather than direct position, velocity, or torque demands.
3 MyoSuite
MyoSuite combines physiologically accurate musculoskeletal models with computationally efficient MuJoCo simulation and contact-rich tasks spanning varied complexity and realistic non-stationary conditions.
- Models: The MyoSim pipeline converts OpenSim models into equivalent MuJoCo models through geometry, moment-arm, and muscle-force optimization.These steps target matching reference-model geometry, moment arms, and muscle force-generating capacity.
- Tasks: MyoSuite includes 204 tasks spanning 9 task families, two difficulty levels, three reset conditions, and combinations of non-stationarity variations.Tasks range from finger and elbow control to key turning, hand poses, reaching, and object holding.
- Tasks: The task suite covers skillful contact-rich behaviors, including coordinating MyoHand finger movements to rotate a key from fixed or random initial configurations.The easy key-turn task reaches a half rotation, whereas the difficult version targets full rotation from different random positions and rotations.
- Realistic non-stationary task-variations: Realistic non-stationarity is modeled through sarcopenia, fatigue, and tendon transfer, altering muscle capacity or force transmission during tasks.Sarcopenia reduces each muscle’s maximal isometric force by 50%; fatigue updates maximal force dynamically; tendon transfer reroutes EIP from the index to the thumb.
4 Experiments
Experiments validate MyoSuite models against OpenSim, establish baseline task-solving behavior, and test adaptation to muscle damage, physiological changes, and exoskeleton assistance.
- Models validation: MuJoCo models matched OpenSim with low moment-arm and force errors while simulating 60x–4000x faster.Elbow and hand moment-arm RMS differences were 0.044 ± 0.09% and 0.38 ± 0.57%; force errors were 2.2 ± 1.4% and 4.1 ± 2.0% Fmax, respectively.
- Baselines: Natural Policy Gradient agents solved MyoSuite tasks, although Baoding-ball manipulation required much higher sample complexity.The baseline covered stationary easy and hard conditions with fixed resets, and successful behaviors included key turning, pen twirling, and Baoding-ball coordination.
- Intrinsic non-stationarity: Sarcopenia increased Brachioradialis activation and recruited synergistic biceps muscles, while fatigue caused progressively greater synergistic contributions.Both perturbations were evaluated on alternating elbow movements after training on random-target reaching.
- Tendon tear: Selective tendon damage revealed redundancy for some thumb muscles but showed that Opponens Pollicis and Flexor Pollicis Longus were critical for key turning.When Opponens Pollicis was torn, Flexor Pollicis Longus could not compensate, reducing key rotations.
- Tendon transfer: After tendon transfer, the previously trained policy failed and required extensive additional training to control the thumb under the remapped activation space.The experiment routed EIP to replace EPL, changing the muscle-to-action relationship.
- Human-robot interaction: Exoskeleton assistance recovered loaded reaching with about 5 degrees of error while reducing muscle activation and force by roughly 60% at 2 Kg.Reported activation reductions included BIClong 60%, BICshort 54%, and BRA 66%; force reductions were 60%, 58%, and 67%.
- Human-robot interaction: With sarcopenia or fatigue, exoskeleton assistance partially recovered static holding and reduced muscle activation and force requirements.Holding errors were 6.03 ± 2.97 for sarcopenia and 11.89 ± 4.03 for fatigue; reductions varied across muscles and conditions.
5 Discussion and Conclusions
The discussion presents MyoSuite as a physiologically realistic, computationally efficient platform for contact-rich motor-control research with non-stationary challenges. Initial policies exhibit physiological compensation, but the models and task variations remain incomplete and require further validation.
- Contributions: MyoSuite combines physiologically realistic models, contact-rich skilled tasks, and non-stationarities including fatigue, sarcopenia, and tendon transfer.The suite is intended to support realistic motor-control challenges and cross-community research.
- Observed behaviors: Basic policies automatically adapt muscle recruitment through antagonistic effects and co-contractions as muscle properties change.These behaviors were observed for flexor-extensor interactions and perturbations such as sarcopenia and fatigue.
- Limitations: Compensation cannot replace muscles with unique hand functions, such as Opponens Pollicis.This limitation constrains how much redundancy can offset selective muscle loss during hand manipulation.
- Limitations: The implemented models are a first approximation requiring further development and validation, and the included tasks and physiological changes cover only a subset of possibilities.The authors identify both model fidelity and scenario coverage as ongoing boundaries.
A MyoSim: A pipeline to generate MuJoCo musculoskeletal models
The model-conversion section introduces the pipeline used to generate MuJoCo musculoskeletal models.
- Pipeline overview: The section explains a pipeline for converting existing musculoskeletal models into MuJoCo models.The passage provides only the section’s stated purpose.
A.1 Musculoskeletal model conversion tool
The conversion tool automatically transforms validated OpenSim musculoskeletal models into MuJoCo through three conversion steps, each followed by validation. It optimizes geometry, muscle wrapping, and force properties to preserve physiological behavior while enabling faster simulation.
- Pipeline overview: The pipeline converts OpenSim models into MuJoCo through three conversion steps, with validation after each step.The stages cover geometry conversion, wrapping optimization, and force-property optimization.
- Geometry conversion: Geometry conversion parses bodies, joints, muscles, tendon pathways, and markers, then checks salient positions across joint configurations.The forward-kinematics validation compares the converted and reference models.
- Wrapping optimization: Wrapping optimization adjusts muscle wrapping locations, sizes, and orientations to match physiologically reasonable pathways and moment-arm behavior.MuJoCo wrapping surfaces approximate OpenSim surfaces that are unavailable in MuJoCo.
- Optimization formulation: The method defines wrapping variables using side-site locations, surface sizes, and orientations, with constraints from kinematics and parameter bounds.The notation includes joint angles, muscle count, joint count, and moment arms.
- Wrapping optimization: Simulated annealing minimizes moment-arm differences between converted MuJoCo and reference OpenSim models across muscles and joint angles.Validation uses moment-arm maps over different joint configurations.
- Force-property optimization: Force-property optimization matches OpenSim and MuJoCo force-length-velocity relationships despite differences in tendon, pennation, and length parameterizations.Differential evolution optimizes force-property parameters, while identical activation dynamics and force-velocity relationships transfer directly.