  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.
