In contrast to humans and animals who naturally execute seamless motions, learning and smoothly executing sequences of actions remains a challenge in robotics. This paper introduces a novel skill-agnostic framework that learns to sequence and blend skills based on differentiable optimization. Our approach encodes sequences of previously-defined skills as quadratic programs (QP), whose parameters determine the relative importance of skills along the task. Seamless skill sequences are then learned from demonstrations by exploiting differentiable optimization layers and a tailored loss formulated from the QP optimality conditions. Via the use of differentiable optimization, our work offers novel perspectives on multitask control. We validate our approach in a pick-and-place scenario with planar robots, a pouring experiment with a real humanoid robot, and a bimanual sweeping task with a human model.

翻译：与自然地执行无缝运动、学习和顺利执行行动序列的人类和动物形成对比的是机器人方面的一项挑战。本文件介绍了一个新的技能认知框架,根据不同的优化方法学习顺序和混合技能。我们的方法将先前界定的技能序列编码为二次程序(QP),其参数决定了在任务过程中技能的相对重要性。然后,通过利用不同的优化层和根据QP最佳性条件制定的量身定制的损失,从演示中学习了无缝技能序列。通过使用不同的优化方法,我们的工作提出了关于多任务控制的新观点。我们用平板机器人验证了我们的方法,用真正的人形机器人来做一个填充实验,用人形机器人来做一个双人形扫荡任务。