  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.
