Learning to Run challenge: Synthesizing physiologically accurate motion
  using deep reinforcement learning
  Synthesizing physiologically-accurate human movement in a variety of
conditions can help practitioners plan surgeries, design experiments, or
prototype assistive devices in simulated environments, reducing time and costs
and improving treatment outcomes. Because of the large and complex solution
spaces of biomechanical models, current methods are constrained to specific
movements and models, requiring careful design of a controller and hindering
many possible applications. We sought to discover if modern optimization
methods efficiently explore these complex spaces. To do this, we posed the
problem as a competition in which participants were tasked with developing a
controller to enable a physiologically-based human model to navigate a complex
obstacle course as quickly as possible, without using any experimental data.
They were provided with a human musculoskeletal model and a physics-based
simulation environment. In this paper, we discuss the design of the
competition, technical difficulties, results, and analysis of the top
controllers. The challenge proved that deep reinforcement learning techniques,
despite their high computational cost, can be successfully employed as an
optimization method for synthesizing physiologically feasible motion in
high-dimensional biomechanical systems.
