让机器人自适应调整抓取力度，实现月壤挖掘的自主控制。
Learning Tool-Aware Adaptive Compliant Control for Autonomous Regolith Excavation
- 通过强化学习在高保真仿真中动态调节刚度与阻尼。
- 使用程序化生成的工具和地形训练，提升泛化能力。
- 加入视觉反馈显著提高挖掘成功率，适合太空任务开发。
自主月壤挖掘是实现地外资源利用、支持人类长期太空生存的关键任务。然而，该任务受颗粒介质复杂交互动力学及机器人需使用多种工具的双重制约。本文提出一种基于模型的强化学习框架，在并行化仿真环境中训练智能体。该环境结合高保真粒子物理模拟与程序化生成技术，构建了涵盖多样月面地形与挖掘工具几何形状的大规模分布数据集。为应对多样性挑战，智能体通过操作空间控制，在每个控制步动态调节自身刚度与阻尼，学习自适应交互策略。实验表明，采用程序化工具分布进行训练对泛化性能至关重要，并能催生复杂的工具感知行为；同时，引入视觉反馈可显著提升任务成功率。本研究验证了一种面向未来太空任务所需鲁棒、多功能自主系统的有效开发方法。
原文摘要 · Abstract (English)
Autonomous regolith excavation is a cornerstone of in-situ resource utilization for a sustained human presence beyond Earth. However, this task is fundamentally hindered by the complex interaction dynamics of granular media and the operational need for robots to use diverse tools. To address these challenges, this work introduces a framework where a model-based reinforcement learning agent learns within a parallelized simulation. This environment leverages high-fidelity particle physics and procedural generation to create a vast distribution of both lunar terrains and excavation tool geometries. To master this diversity, the agent learns an adaptive interaction strategy by dynamically modulating its own stiffness and damping at each control step through operational space control. Our experiments demonstrate that training with a procedural distribution of tools is critical for generalization and enables the development of sophisticated tool-aware behavior. Furthermore, we show that augmenting the agent with visual feedback significantly improves task success. These results represent a validated methodology for developing the robust and versatile autonomous systems required for the foundational tasks of future space missions.
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