Researchers have created a tool that will make simulations more realistic. A team at the University of California Berkeley used deep reinforcement learning in order to let computer simulations mimic natural human movements. Their tool will allow video game characters to move and animated movie scenes to play out with the fluidity and rhythm of the real world. The recreations of natural movements will make simulations of animals and humans much less clumsy, a report on the new technology said. The feat will even improve scenes that include complex acrobatic feats, such martial arts and break dancing.

Motion control problems have become standard benchmarks for reinforcement learning, and deep RL methods have been shown to be effective for a diverse suite of tasks ranging from manipulation to locomotion. However, characters trained with deep RL often exhibit unnatural behaviours, bearing artifacts such as jittering, asymmetric gaits, and excessive movement of limbs. Can we train our characters to produce more natural behaviours? A wealth of inspiration can be drawn from computer graphics, where the physics-based simulation of natural movements have been a subject of intense study for decades. The greater emphasis placed on motion quality is often motivated by applications in film, visual effects, and games.