Abstract
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.
Abstract (translated)
自治机器人手臂操作器具有将行星探索和原地资源利用任务更加高效和产出的潜力，因为操作器可以自己处理物体并执行目标特定的动作。我们训练了一个操作器来自动研究它以前没有接触过的物体，例如行星岩石。这是通过在模拟行星环境中使用因果机器学习来实现的。在这里，操作器与物体互动并根据不同的因果因素对它们进行分类。这些参数，如质量或摩擦系数，因果地决定了它们互动的结果。通过强化学习，操作器学会了以揭示潜在的因果因素的方式进行互动。我们证明了这种方法在没有任何先验知识或之前收集的训练数据的情况下也能够有效。我们在行星探索条件下进行训练，使用 realistic operator models。
URL
https://arxiv.org/abs/2403.00470
