Learning Energy-Efficient Air--Ground Actuation for Hybrid Robots on Stair-Like Terrain
Hybrid aerial--ground robots can use thrust to cross obstacles that impede wheel-driven motion, but deciding how much thrust to apply during contact remains challenging. We present an energy-aware reinforcement learning framework that jointly commands wheels, tilt servos and propellers through a single continuous policy, without prescribing locomotion modes. Hardware-calibrated power models penalise estimated electrical energy, while a terrain curriculum and a terminal reward for upright, settled arrivals support learning of thrust-assisted climbing. In simulation, continuous thrust allocation improves single-step clearance over fixed-thrust and mode-switching baselines as steps become ta...
| Authors | Jiaxing Li, Ishaan Bhimwal, Wen Tian et al. |
|---|---|
| Primary category | cs.RO |
| Published | 13 March 2026 |
| Last updated at source | 19 September 2026 |
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