Physics-informed reinforcement learning for motion control of a fish-like swimming robot.

Motion control of fish-like swimming robots presents many challenges due to the unstructured environment and unmodelled governing physics of the fluid-robot interaction. Commonly used low-fidelity control models using simplified formulas for drag and lift forces do not capture key physics that can play an important role in the dynamics of small-sized robots with limited actuation. Deep Reinforcement Learning (DRL) holds considerable promise for motion control of robots with complex dynamics. Reinforcement learning methods require large amounts of training data exploring a large subset of the relevant state space, which can be expensive, time consuming, or unsafe to obtain. Data from simulations can be used in the initial stages of DRL, but in the case of swimming robots, the complexity of fluid-body interactions makes large numbers of simulations infeasible from the perspective of time and computational resources. Surrogate models that capture the primary physics of the system can be a useful starting point for training a DRL agent which is subsequently transferred to train with a higher fidelity simulation. We demonstrate the utility of such physics-informed reinforcement learning to train a policy that can enable velocity and path tracking for a planar swimming (fish-like) rigid Joukowski hydrofoil. This is done through a curriculum where the DRL agent is first trained to track limit cycles in a velocity space for a representative nonholonomic system, and then transferred to train on a small simulation data set of the swimmer. The results show the utility of physics-informed reinforcement learning for the control of fish-like swimming robots.

Proprioceptive wake classification by a body with a passive tail.

The remarkable ability of some marine animals to identify flow structures and parameters using complex non-visual sensors, such as lateral lines of fish and the whiskers of seals, has been an area of investigation for researchers looking to apply this ability to artificial robotic swimmers, which could lead to improvements in autonomous navigation and efficiency. Several species of fish in particular have been known to school effectively, even when blind. Beyond specialized sensors like the lateral lines, it is now known that some fish use purely proprioceptive sensing, using the kinematics of their fins or tails to sense their surroundings. In this paper we show that the kinematics of a body with a passive tail encode information about the ambient flow, which can be deciphered through machine learning. We demonstrate this with experimental data of the angular velocity of a hydrofoil with a passive tail that lies in the wake generated by an upstream oscillating body. Using convolutional neural networks, we show that with the kinematic data from the downstream body with a tail, the wakes can be better classified than in the case of a body without a tail. This superior sensing ability exists for a body with a tail, even if only the kinematics of the main body are used as input for the machine learning. This shows that beyond generating 'additional inputs', passive tails modulate the response of the main body in manner that is useful for hydrodynamic sensing. These findings have clear application for improving the sensing abilities of bioinspired swimming robots.