Agile and Controllable Omnidirectional Fast-Start Maneuvers of Robotic Fish Via Bio-Inspired Reinforcement Learning

Abstract

Fast-start maneuvers—exemplified by the C-start in fish—represent a highly agile and very attractive locomotor strategy that requires precise multi-joint coordination under conditions of unsteady fluid dynamics, and has evolved through extensive predator–prey interactions in natural environments. Replicating such maneuvers in robotic fish is challenging due to strong fluid–structure nonlinearities, instantaneous dynamics, and complex vortex interactions. Prior approaches were limited by their dependence on specialized materials, lack of active controllability, incompatibility with mechanical structures, and inability to generate sufficient forward propulsion. Here, we propose a deep reinforcement learning method for multi-joint robotic fish that embeds key biological features of C-start maneuvers—burst acceleration, rapid directional adjustment, and two-stage bend-and-stretch motion—into the reward and observation design. By training in a physically consistent, high-performance Computational Fluid Dynamics (CFD) solver, the agent autonomously discovers effective launch strategies without requiring explicit models or real fish data. The resulting policies not only reproduce C-start-like motions and achieve fully controllable directional fast-starts, but also significantly expand the maneuvering potential of robotic fish, enabling higher velocities, greater displacement, and more agile motion than state-of-the-art methods. This biologically inspired and generalizable method demonstrates the promise of integrating biological principles into reinforcement learning to unlock advanced, high-acceleration capabilities in multi-joint aquatic robots.