SKATER: Synthesized Kinematics for Advanced Traversing Efficiency on a Humanoid Robot via Roller Skate Swizzles

TL;DR

This paper introduces a novel humanoid robot equipped with roller skate wheels and a deep reinforcement learning control framework, achieving more energy-efficient and impact-reducing locomotion compared to traditional walking.

Contribution

It presents a new robot design with passive wheels and a reinforcement learning-based control method for roller skating gait, improving energy efficiency and impact reduction.

Findings

75.86% reduction in Impact Intensity

63.34% reduction in Cost of Transport

Demonstrated smooth and efficient roller skating gait

Abstract

Although recent years have seen significant progress of humanoid robots in walking and running, the frequent foot strikes with ground during these locomotion gaits inevitably generate high instantaneous impact forces, which leads to exacerbated joint wear and poor energy utilization. Roller skating, as a sport with substantial biomechanical value, can achieve fast and continuous sliding through rational utilization of body inertia, featuring minimal kinetic energy loss. Therefore, this study proposes a novel humanoid robot with each foot equipped with a row of four passive wheels for roller skating. A deep reinforcement learning control framework is also developed for the swizzle gait with the reward function design based on the intrinsic characteristics of roller skating. The learned policy is first analyzed in simulation and then deployed on the physical robot to demonstrate the smoothness and efficiency of the swizzle gait over traditional bipedal walking gait in terms of Impact Intensity and Cost of Transport during locomotion. A reduction of $75.86\%$ and $63.34\%$ of these two metrics indicate roller skating as a superior locomotion mode for enhanced energy efficiency and joint longevity.