Learning from humans allows non-experts to program robots with ease, lowering the resources required to build complex robotic solutions. Nevertheless, such data-driven approaches often lack the ability to provide guarantees regarding their learned behaviors, which is critical for avoiding failures and/or accidents. In this work, we focus on reaching/point-to-point motions, where robots must always reach their goal, independently of their initial state. This can be achieved by modeling motions as dynamical systems and ensuring that they are globally asymptotically stable. Hence, we introduce a novel Contrastive Learning loss for training Deep Neural Networks (DNN) that, when used together with an Imitation Learning loss, enforces the aforementioned stability in the learned motions. Differently from previous work, our method does not restrict the structure of its function approximator, enabling its use with arbitrary DNNs and allowing it to learn complex motions with high accuracy. We validate it using datasets and a real robot. In the former case, motions are 2 and 4 dimensional, modeled as first- and second-order dynamical systems. In the latter, motions are 3, 4, and 6 dimensional, of first and second order, and are used to control a 7DoF robot manipulator in its end effector space and joint space. More details regarding the real-world experiments are presented in: \url{https://youtu.be/OM-2edHBRfc}.

翻译：从人类中学习使得非专家能够轻松编程机器人，降低了构建复杂机器人解决方案所需的资源。然而，此类数据驱动方法往往无法为其学习的行为提供保证，这对于避免故障和/或事故至关重要。在本工作中，我们聚焦于到达/点对点运动，其中机器人必须始终达到其目标，与其初始状态无关。这可以通过将运动建模为动力系统并确保其全局渐近稳定来实现。因此，我们引入了一种新颖的对比学习损失函数，用于训练深度神经网络（DNN），该损失函数与模仿学习损失一起使用时，能够强制所学习的运动具有上述稳定性。与先前工作不同，我们的方法不限制其函数逼近器的结构，从而能将其与任意DNN结合使用，并能够高精度地学习复杂运动。我们使用数据集和真实机器人对其进行了验证。在前一种情况下，运动是2维和4维的，并建模为一阶和二阶动力系统。在后一种情况下，运动是3维、4维和6维的，分为一阶和二阶，用于在末端执行器空间和关节空间中控制一个7自由度机器人操作臂。关于真实世界实验的更多细节请参见：\url{https://youtu.be/OM-2edHBRfc}。