Learning to Run challenge: Synthesizing physiologically accurate motion using deep reinforcement learning
This addresses the challenge of generating realistic human movement for medical and engineering practitioners, but it is incremental as it applies existing deep reinforcement learning methods to a new domain-specific problem.
The paper tackled the problem of synthesizing physiologically-accurate human motion for applications like surgery planning and device design by posing it as a competition where participants developed controllers using deep reinforcement learning to navigate a complex obstacle course without experimental data. The result showed that deep reinforcement learning can successfully optimize motion in high-dimensional biomechanical systems, though it is computationally expensive.
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.