Learning Energy-Efficient Air--Ground Actuation for Hybrid Robots on Stair-Like Terrain
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911548694331392 |
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| author | Li, Jiaxing Tian, Wen Xu, Xinhang Yuan, Junbin Scherer, Sebastian Cao, Muqing |
| author_facet | Li, Jiaxing Tian, Wen Xu, Xinhang Yuan, Junbin Scherer, Sebastian Cao, Muqing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_26687 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning Energy-Efficient Air--Ground Actuation for Hybrid Robots on Stair-Like Terrain Li, Jiaxing Tian, Wen Xu, Xinhang Yuan, Junbin Scherer, Sebastian Cao, Muqing Robotics Artificial Intelligence 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. |
| title | Learning Energy-Efficient Air--Ground Actuation for Hybrid Robots on Stair-Like Terrain |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2603.26687 |