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 of providing 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: https://youtu.be/OM-2edHBRfc.

翻译：从人类学习中，非专业人员可以轻松地为机器人编程，从而降低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缺乏对其学习行为提供保证的能力，这对于避免故障和/或事故至关重要。本研究聚焦于到达/点对点运动，其中机器人无论初始状态如何，必须始终到达目标。这可以通过将运动建模为动力学系统并确保其全局渐近稳定来实现。为此，我们引入了一种新颖的对比学习损失函数来训练深度神经网络（DNN），当与模仿学习损失联合使用时，该损失函数能够强制所学习运动具有上述稳定性。与以往工作不同，我们的方法不限制函数逼近器的结构，使其能够与任意DNN结合使用，并能够高精度学习复杂运动。我们通过数据集和真实机器人进行验证。在前者中，运动为2维和4维，建模为一阶和二阶动力学系统。在后者中，运动为3维、4维和6维，属于一阶和二阶，并用于在末端执行器空间和关节空间控制一个7自由度机器人机械臂。有关真实世界实验的更多详情可参见：https://youtu.be/OM-2edHBRfc。