论文标题
通过可区分的优化学习对机器人技能进行顺序和融合机器人技能
Learning to Sequence and Blend Robot Skills via Differentiable Optimization
论文作者
论文摘要
与自然执行无缝动作的人类和动物相反，在机器人技术中，学习并顺利执行动作序列仍然是一个挑战。本文介绍了一个新颖的技能无关框架，该框架学会根据可区分的优化来对技能进行顺序和融合。我们的方法将以前定义的技能的序列编码为二次程序（QP），其参数决定了技能在任务中的相对重要性。然后，通过利用可区分优化层和根据QP最佳条件提出的量身定制的损失，从演示中学到了无缝的技能序列。通过使用可区分的优化，我们的工作提供了有关多任务控制的新观点。我们在使用Planar Robots的采摘场景中验证了我们的方法，对真实的人形机器人的倾泻实验以及具有人类模型的双人横扫任务。
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.
