Olaf: Bringing an Animated Character to Life in the Physical World

TL;DR

This paper presents Olaf, a costumed robotic character brought to life through reinforcement learning, with innovative mechanical design and control strategies to achieve realistic motion and reduce noise and overheating.

Contribution

The work introduces a novel approach combining mechanical design and reinforcement learning for animating a stylized character in the physical world.

Findings

Reinforcement learning effectively controls Olaf's motion in simulation and hardware.

Design strategies reduce impact noise and prevent actuator overheating.

The resulting character exhibits highly believable and stylized movement.

Abstract

Animated characters often move in non-physical ways and have proportions that are far from a typical walking robot. This provides an ideal platform for innovation in both mechanical design and stylized motion control. In this paper, we bring Olaf to life in the physical world, relying on reinforcement learning guided by animation references for control. To create the illusion of Olaf's feet moving along his body, we hide two asymmetric legs under a soft foam skirt. To fit actuators inside the character, we use spherical and planar linkages in the arms, mouth, and eyes. Because the walk cycle results in harsh contact sounds, we introduce additional rewards that noticeably reduce impact noise. The large head, driven by small actuators in the character's slim neck, creates a risk of overheating, amplified by the costume. To keep actuators from overheating, we feed temperature values as additional inputs to policies, introducing new rewards to keep them within bounds. We validate the efficacy of our modeling in simulation and on hardware, demonstrating an unmatched level of believability for a costumed robotic character.