Agile and Controllable Omnidirectional Fast-Start Maneuvers of Robotic Fish Via Bio-Inspired Reinforcement Learning
Xu Huang, Xiaozhu Lin, Xiaopei Liu, Yang Wang
AI summary
Problem
Replicating the rapid, highly agile C-start fast-start maneuvers of natural fish in robotic systems is hindered by strong fluid-structure nonlinearities, lack of active controllability, and insufficient forward propulsion in existing approaches.
Approach
The method embeds key biological C-start features into a deep reinforcement learning reward and observation design, training an agent in a high-performance CFD simulator to autonomously discover effective multi-joint control policies without explicit models.
Key results
- Achieves fully controllable omnidirectional fast-start maneuvers with lower directional errors than baselines
- Generates significantly higher peak forward velocities and greater displacement projections
- Reproduces biologically plausible C-start kinematics and coherent vortex structures
- Trains efficiently in a physically consistent CFD simulator without requiring explicit dynamic models or real fish data
Why it matters
Integrating biological principles into reinforcement learning unlocks advanced, high-acceleration aquatic locomotion in multi-joint robots, advancing biomimetic robotics and underwater maneuverability.
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.