74 research outputs found
On Weight-Prioritized Multi-Task Control of Humanoid Robots
International audienceWe propose a formal analysis with some theoretical properties of weight-prioritized multi-task inverse-dynamics-like control of humanoid robots, being a case of redundant " ma-nipulators " with a non-actuated free-floating base and multiple unilateral frictional contacts with the environment. The controller builds on a weighted sum scalarization of a multiobjective optimization problem under equality and inequality constraints, which appears as a straightforward solution to account for state and control input viability constraints characteristic of humanoid robots that were usually absent from early existing pseudo-inverse and null-space projection-based prioritized multi-task approaches. We argue that our formulation is indeed well founded and justified from a theoretical standpoint, and we propose an analysis of some stability properties of the approach: Lyapunov stability is demonstrated for the closed-loop dynamical system that we analytically derive in the unconstrained multiob-jective optimization case. Stability in terms of solution existence, uniqueness, continuity, and robustness to perturbations, is then formally demonstrated for the constrained quadratic program
Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems
Many modern nonlinear control methods aim to endow systems with guaranteed
properties, such as stability or safety, and have been successfully applied to
the domain of robotics. However, model uncertainty remains a persistent
challenge, weakening theoretical guarantees and causing implementation failures
on physical systems. This paper develops a machine learning framework centered
around Control Lyapunov Functions (CLFs) to adapt to parametric uncertainty and
unmodeled dynamics in general robotic systems. Our proposed method proceeds by
iteratively updating estimates of Lyapunov function derivatives and improving
controllers, ultimately yielding a stabilizing quadratic program model-based
controller. We validate our approach on a planar Segway simulation,
demonstrating substantial performance improvements by iteratively refining on a
base model-free controller
Reliably-stabilizing piecewise-affine neural network controllers
A common problem affecting neural network (NN) approximations of model
predictive control (MPC) policies is the lack of analytical tools to assess the
stability of the closed-loop system under the action of the NN-based
controller. We present a general procedure to quantify the performance of such
a controller, or to design minimum complexity NNs with rectified linear units
(ReLUs) that preserve the desirable properties of a given MPC scheme. By
quantifying the approximation error between NN-based and MPC-based
state-to-input mappings, we first establish suitable conditions involving two
key quantities, the worst-case error and the Lipschitz constant, guaranteeing
the stability of the closed-loop system. We then develop an offline,
mixed-integer optimization-based method to compute those quantities exactly.
Together these techniques provide conditions sufficient to certify the
stability and performance of a ReLU-based approximation of an MPC control law
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