Next iterations of quadratic programming for adaptive and robust motion control

Abstract¶

Convex quadratic programming (QP) has become a major item in the robotics toolbox, with well-known applications including whole-body control, model predictive control (MPC), contact planning and state estimation. Current challenges when solving QP-formulated problems include feasibility (ensuring that a solution exists, e.g. when some problem parameters come from measurements), recursive feasibility (in MPC: ensuring the system does not steer towards unfeasible problems) and real-time performance. In this talk, we review new features brought by an upcoming generation of QP solvers. We will focus in particular on ProxQP, which can handle non-convex or unfeasible problems and always returns a principled solution. We will further develop how this enables the inclusion of differentiable QP layers in end-to-end trainable control pipelines.

Content¶

	Slides
	QP solvers benchmark
	ProxQP (includes QPLayer)
	PROXQP: an Efficient and Versatile Quadratic Programming Solver for Real-Time Robotics Applications and Beyond
	QPLayer: efficient differentiation of convex quadratic optimization