Abstract
We present a technique for automatically synthesizing walking and running controllers for physically-simulated 3D humanoid characters. The sagittal hip, knee, and ankle degrees-of-freedom are actuated using a set of eight Hill-type musculotendon models in each leg, with biologically-motivated control laws. The parameters of these control laws are set by an optimization procedure that satisfies a number of locomotion task terms while minimizing a biological model of metabolic energy expenditure. We show that the use of biologically-based actuators and objectives measurably increases the realism of gaits generated by locomotion controllers that operate without the use of motion capture data, and that metabolic energy expenditure provides a simple and unifying measurement of effort that can be used for both walking and running control optimization.
1 Introduction
The development of physics-based locomotion controllers de novo, independent from stock motion data, has been a long-standing objective in computer graphics research and has seen resurgence in recent years. Despite impressive progress, the gaits produced by existing controllers fall short of the natural appearance of human locomotion. For example, physicsbased walking controllers that do not rely on motion capture data commonly produce walking motion with exaggerated hip flexion which appears more crouched and less fluid than typical human walking.
7 Discussion
We have presented a biologically-motivated control parameterization that can be used to automatically generate 3D human-like walking and running controllers of different speeds. Controllers are optimized to satisfy a set of high-level task terms while minimizing an effort term based on modeling the rate of metabolic energy expenditure. Notably, walking and running emerge from the same optimization process simply by changing the target velocity and initialization. Through comparisons to kinematic and torque data of human walking, we show that our results adopt a human-like torque generation strategy while producing kinematic data significantly closer to humans than previous work. Our work demonstrates the importance of modeling constraints on torque generation due to muscle physiology, both in restricting the space of possible torque trajectories and in providing a realistic model of effort.
