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.

翻译：在非地球环境中部署腿式机器人面临诸多挑战，包括复杂地形交互、能量与热约束等因素。针对月球探测四足机器人的有效机械设计，需要审慎考虑电机扭矩、能量消耗及运输成本。月球表面覆盖着颗粒状风化层，这会显著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应用于颗粒表面等柔性接触环境时同样失效，可能导致系统失稳与跟踪误差。本研究中，将月面颗粒岩屑与机器人足部的物理接触模型应用于强化学习训练的运动仿真环境。通过对比在刚性接触与柔性接触环境下训练的控制策略，分析步态及运动性能指标。研究表明，模拟风化层表面的柔性接触对基于强化学习的训练构成额外挑战，不仅会产生性质差异显著的步态模式，还会增加整体能量消耗。