通过预测能量选择最优步态，让轮式四足机器人更省电。
Energy-Efficient Omnidirectional Locomotion for Wheeled Quadrupeds via Predictive Energy-Aware Nominal Gait Selection
- 用预测网络提前算出不同步态的耗能，选最省电的主步态。
- 在主步态上用强化学习微调动作，能耗降35%且速度不掉。
- 适合做节能移动的轮腿机器人，尤其复杂地形下表现稳。
轮式腿足机器人兼具轮子的高效与腿的灵活，但在多样环境中面临显著的能耗优化挑战。本文提出分层控制框架，融合预测性功率建模与残差强化学习，以优化轮式四足机器人的全向运动效率。方法采用新型功率预测网络，可对未来1秒内不同步态的能耗进行预估，从而智能选择最节能的名义步态。随后，强化学习策略生成对名义步态的残差调整，精细优化机器人动作，在保持能耗效率的同时兼顾性能目标。对比实验表明，该方法相比固定步态方案能耗降低最高达35%，同时维持相近的速度跟踪性能。通过在改装版Unitree Go1平台上开展的大量仿真与实机测试验证了框架的鲁棒性，即使在外部扰动下也能稳定运行。视频与实现细节见 https://sites.google.com/view/switching-wpg。
原文摘要 · Abstract (English)
Wheeled-legged robots combine the efficiency of wheels with the versatility of legs, but face significant energy optimization challenges when navigating diverse environments. In this work, we present a hierarchical control framework that integrates predictive power modeling with residual reinforcement learning to optimize omnidirectional locomotion efficiency for wheeled quadrupedal robots. Our approach employs a novel power prediction network that forecasts energy consumption across different gait patterns over a 1-second horizon, enabling intelligent selection of the most energy-efficient nominal gait. A reinforcement learning policy then generates residual adjustments to this nominal gait, fine-tuning the robot's actions to balance energy efficiency with performance objectives. Comparative analysis shows our method reduces energy consumption by up to 35\% compared to fixed-gait approaches while maintaining comparable velocity tracking performance. We validate our framework through extensive simulations and real-world experiments on a modified Unitree Go1 platform, demonstrating robust performance even under external disturbances. Videos and implementation details are available at \href{https://sites.google.com/view/switching-wpg}{https://sites.google.com/view/switching-wpg}.
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