  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.
