Sensing flow gradients is necessary for learning autonomous underwater navigation

Aquatic animals are much better at underwater navigation than robotic vehicles. Robots face major challenges in deep water because of their limited access to global positioning signals and flow maps. These limitations, and the changing nature of water currents, support the use of reinforcement learning approaches, where the navigator learns through trial-and-error interactions with the flow environment. But is it feasible to learn underwater navigation in the agent’s Umwelt, without any land references? Here, we tasked an artificial swimmer with learning to reach a specific destination in unsteady flows by relying solely on egocentric observations, collected through on-board flow sensors in the agent’s body frame, with no reference to a geocentric inertial frame. We found that while sensing local flow velocities is sufficient for geocentric navigation, successful egocentric navigation requires additional information of local flow gradients. Importantly, egocentric navigation strategies obey rotational symmetry and are more robust in unfamiliar conditions and flows not experienced during training. Our work expands underwater robot-centric learning, helps explain why aquatic organisms have arrays of flow sensors that detect gradients, and provides physics-based guidelines for transfer learning of learned policies to unfamiliar and diverse flow environments. Aquatic animals outperform robotic vehicles in underwater navigation due to robots’ limited access to GPS and flow maps in deep water. The authors report that to successfully learn navigation, an agent must sense both local flows and flow gradients, enabling adaptable and robust policies under unfamiliar conditions.