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

2026-03-13

arXiv.org

AI Summary

研究提出一种能量感知的强化学习框架，通过单个连续策略控制轮子、倾斜伺服器和螺旋桨，以解决混合空中-地面机器人在阶梯状地形中高效使用推力的问题。

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.

Problem

• Hybrid aerial-ground robots
• thrust allocation
• energy efficiency
• stair-like terrain
• obstacle crossing

Innovation

• energy-aware reinforcement learning
• continuous policy
• thrust-assisted climbing
• terrain curriculum
• power models