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

TL;DR

This paper presents an energy-aware reinforcement learning approach for hybrid aerial-ground robots that optimizes actuation to reduce energy consumption on stair-like terrain.

Contribution

It introduces a unified policy that coordinates propellers, wheels, and tilt servos without predefined modes, trained with hardware-calibrated models for real-world deployment.

Findings

Simulation shows 4x lower energy than propeller-only control.

On hardware, achieves 38% lower power than rule-based controllers.

Policy successfully climbs 8cm gaps with reduced energy use.

Abstract

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. We train policies from proprioception and a local height scan in Isaac Lab with parallel environments, using hardware-calibrated thrust/power models so the reward penalizes true electrical energy. The learned policy discovers thrust-assisted driving that blends aerial thrust and ground traction. In simulation it achieves about 4 times lower energy than propeller-only control. We transfer the policy to a DoubleBee prototype on an 8cm gap-climbing task; it achieves 38% lower average power than a rule-based decoupled controller. These results show that efficient hybrid actuation can emerge from learning and deploy on hardware.