Abstract Equipping robots with a physical, brain-inspired computing unit is a key step towards achieving true autonomy in intelligent machines; however, the practical realization of such neuromorphic hardware remains a major challenge. Because both software- and hardware-based spiking neural network approaches demand substantial training and energy consumption, an architecturally simple neuromorphic platform is required to enable scalable and energy-efficient robotics. Here, we experimentally demonstrate robotic control driven by a neuromorphic hardware based on physical wave interactions. Using a water-based experimental system that provides direct visual access to wave-network dynamics, we establish a wave-based reservoir computing framework that achieves near-perfect accuracy in robotic vehicle obstacle recognition, followed by real-time autonomous motion with obstacle avoidance controlled entirely by this wave-based hardware. Through micromagnetic simulations, we extend this concept to electrically excited spin waves in a magnetic nanodevice operating at gigahertz frequencies, outlining an approach toward solid-state robotic chips based on wave computing. These results position wave-based computation as a scalable and energy-efficient alternative to conventional digital neuromorphic architectures for next-generation intelligent machines.