Related papers: Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functions

Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functions

URL: http://arxiv.org/abs/2004.07584v2
Date: Thu, 4 Jun 2020 17:07:52 GMT
Title: Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functions
Authors: Jason Choi, Fernando Casta\~neda, Claire J. Tomlin, Koushil Sreenath
Abstract summary: Reinforcement learning framework learns the model uncertainty present in the CBF and CLF constraints. RL-CBF-CLF-QP addresses the problem of model uncertainty in the safety constraints.
Score: 96.63967125746747
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: In this paper, the issue of model uncertainty in safety-critical control is addressed with a data-driven approach. For this purpose, we utilize the structure of an input-ouput linearization controller based on a nominal model along with a Control Barrier Function and Control Lyapunov Function based Quadratic Program (CBF-CLF-QP). Specifically, we propose a novel reinforcement learning framework which learns the model uncertainty present in the CBF and CLF constraints, as well as other control-affine dynamic constraints in the quadratic program. The trained policy is combined with the nominal model-based CBF-CLF-QP, resulting in the Reinforcement Learning-based CBF-CLF-QP (RL-CBF-CLF-QP), which addresses the problem of model uncertainty in the safety constraints. The performance of the proposed method is validated by testing it on an underactuated nonlinear bipedal robot walking on randomly spaced stepping stones with one step preview, obtaining stable and safe walking under model uncertainty.

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