Optimal Gait Design for a Soft Quadruped Robot via Multi-fidelity Bayesian Optimization

TL;DR

This paper presents a multi-fidelity Bayesian optimization method for designing optimal gaits in a soft quadruped robot, effectively bridging the simulation-reality gap and enabling real-time adaptive locomotion control.

Contribution

It introduces a multi-fidelity Bayesian optimization framework combined with edge computing for efficient, adaptive gait design in soft quadruped robots, addressing the reality gap.

Findings

Successful gait optimization for physical deployment

Effective reduction of simulation-to-reality gap

Enhanced real-time control via edge computing

Abstract

This study focuses on the locomotion capability improvement in a tendon-driven soft quadruped robot through an online adaptive learning approach. Leveraging the inverse kinematics model of the soft quadruped robot, we employ a central pattern generator to design a parametric gait pattern, and use Bayesian optimization (BO) to find the optimal parameters. Further, to address the challenges of modeling discrepancies, we implement a multi-fidelity BO approach, combining data from both simulation and physical experiments throughout training and optimization. This strategy enables the adaptive refinement of the gait pattern and ensures a smooth transition from simulation to real-world deployment for the controller. Moreover, we integrate a computational task off-loading architecture by edge computing, which reduces the onboard computational and memory overhead, to improve real-time control performance and facilitate an effective online learning process. The proposed approach successfully achieves optimal walking gait design for physical deployment with high efficiency, effectively addressing challenges related to the reality gap in soft robotics.