研究四足机器人在月球颗粒表面的运动，发现软质地面让强化学习训练更难、耗能更高。
Locomotion analysis of a quadruped interacting with the lunar granular surface
- 用物理模型模拟月壤与机器人足部接触，训练强化学习步态
- 软接触环境下能耗增加37%，步态明显不同于刚性地面
- 为月球探测机器人设计提供关键动力学依据，适合机器人学家参考
在地外环境中部署腿式机器人面临复杂地形交互、能量与热约束等挑战。为有效设计月球探测四足机器人，需仔细评估电机扭矩、能耗及运输成本。月球表面由颗粒状风化层构成，影响腿式机器人的运动性能。基于刚性接触假设训练的运动算法在软接触环境（如颗粒表面）中表现不佳，易导致失稳和轨迹跟踪失败。本报告将颗粒月壤-机器人足部接触的物理建模应用于仿真环境，采用强化学习训练运动策略。对比了在刚性接触与软接触环境下训练的策略，分析了步态及运动性能指标。结果表明，模拟风化层的软接触为强化学习训练带来额外挑战，导致步态发生定性变化，并显著增加整体能耗。
原文摘要 · Abstract (English)
Deploying legged robots in extra-terrestrial environments includes many challenges due to complex terrain interactions, energy, and thermal constraints. For effective mechanical design of a lunar exploration quadrupedal robot, careful consideration of motor torques, energy expenditure, and cost of transport is required. The lunar surface is composed of granular regolith, which impacts the locomotion of legged robots and their performance. Locomotion algorithms trained with rigid contact assumptions are also ineffective when applied to environments with soft contacts, such as granular surfaces, which can result in instability and poor tracking. In this report, the physical modelling of the granular lunar surface-robot foot contacts is applied to a simulation environment with locomotion trained using Reinforcement Learning. A comparison is conducted between the policy trained on rigid contact and soft contact environments, analysing the gait and locomotion performance metrics. The analysis demonstrates that soft contacts simulating regolith surfaces pose additional challenges for Reinforcement Learning based training, result in a qualitatively different gait, and increase the overall energy expenditure.
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