Learning Energy-Efficient Air--Ground Actuation for Hybrid Robots on Stair-Like Terrain

ArXi:2603.26687v1 Announce Type: cross Hybrid aerial--ground robots offer both traversability and endurance, but stair-like discontinuities create a trade-off: wheels alone often stall at edges, while flight is energy-hungry for small height gains. We propose an energy-aware reinforcement learning framework that trains a single continuous policy to coordinate propellers, wheels, and tilt servos without predefined aerial and ground modes.