Autonomous robotic arm manipulators have the potential to make planetary exploration and in-situ resource utilization missions more time efficient and productive, as the manipulator can handle the objects itself and perform goal-specific actions. We train a manipulator to autonomously study objects of which it has no prior knowledge, such as planetary rocks. This is achieved using causal machine learning in a simulated planetary environment. Here, the manipulator interacts with objects, and classifies them based on differing causal factors. These are parameters, such as mass or friction coefficient, that causally determine the outcomes of its interactions. Through reinforcement learning, the manipulator learns to interact in ways that reveal the underlying causal factors. We show that this method works even without any prior knowledge of the objects, or any previously-collected training data. We carry out the training in planetary exploration conditions, with realistic manipulator models.

自主机器臂操纵器在行星探索和原位资源利用任务中具有提高时间效率和生产效率的潜力，并且操作器可以自行处理对象并执行目标特定动作。我们利用因果机器学习在模拟的行星环境中训练操纵器自主研究一些它没有先前知识的对象，如行星岩石，并且通过不同的因果因素对它们进行分类，这些因素包括质量或摩擦系数等，这些因素决定了其相互作用的结果。我们通过强化学习使操纵器学会以揭示潜在因果因素的方式相互作用，我们证明了即使没有任何对象的先前知识或以前收集的训练数据，该方法也能够奏效。我们在真实的操纵器模型下在行星探索条件下进行训练。

使用因果机器学习的行星任务自主机械臂操作