[2603.26687] Learning Energy-Efficient Air--Ground Actuation for Hybrid Robots on Stair-Like Terrain

Computer Science > Robotics arXiv:2603.26687 (cs) [Submitted on 13 Mar 2026] Title:Learning Energy-Efficient Air--Ground Actuation for Hybrid Robots on Stair-Like Terrain Authors:Jiaxing Li, Wen Tian, Xinhang Xu, Junbin Yuan, Sebastian Scherer, Muqing Cao View a PDF of the paper titled Learning Energy-Efficient Air--Ground Actuation for Hybrid Robots on Stair-Like Terrain, by Jiaxing Li and 5 other authors View PDF HTML (experimental) 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. Subjects: Robo...