Learning Robust Hybrid Control Barrier Functions for Uncertain Systems

• URL: http://arxiv.org/abs/2101.06492v1
• Date: Sat, 16 Jan 2021 17:53:35 GMT
• Title: Learning Robust Hybrid Control Barrier Functions for Uncertain Systems
• Authors: Alexander Robey, Lars Lindemann, Stephen Tu, and Nikolai Matni
• Abstract summary: We propose robust hybrid control barrier functions as a means to synthesize control laws that ensure robust safety. Based on this notion, we formulate an optimization problem for learning robust hybrid control barrier functions from data. Our techniques allow us to safely expand the region of attraction of a compass gait walker that is subject to model uncertainty.
• Score: 68.30783663518821
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The need for robust control laws is especially important in safety-critical applications. We propose robust hybrid control barrier functions as a means to synthesize control laws that ensure robust safety. Based on this notion, we formulate an optimization problem for learning robust hybrid control barrier functions from data. We identify sufficient conditions on the data such that feasibility of the optimization problem ensures correctness of the learned robust hybrid control barrier functions. Our techniques allow us to safely expand the region of attraction of a compass gait walker that is subject to model uncertainty.

Related papers

• Reinforcement Learning-based Receding Horizon Control using Adaptive Control Barrier Functions for Safety-Critical Systems [14.166970599802324]
  Optimal control methods provide solutions to safety-critical problems but easily become intractable. We propose a Reinforcement Learning-based Receding Horizon Control approach leveraging Model Predictive Control. We validate our method by applying it to the challenging automated merging control problem for Connected and Automated Vehicles.
  arXiv  Detail & Related papers  (2024-03-26T02:49:08Z)
• Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical Systems [15.863561935347692]
  We develop provably safe and convergent reinforcement learning algorithms for control of nonlinear dynamical systems. Recent advances at the intersection of control and RL follow a two-stage, safety filter approach to enforcing hard safety constraints. We develop a single-stage, sampling-based approach to hard constraint satisfaction that learns RL controllers enjoying classical convergence guarantees.
  arXiv  Detail & Related papers  (2024-03-06T19:39:20Z)
• Stable and Safe Reinforcement Learning via a Barrier-Lyapunov Actor-Critic Approach [1.8924647429604111]
  Barrier-Lyapunov Actor-Critic (BLAC) framework helps maintain the aforementioned safety and stability for the system. Additional backup controller is introduced in case the RL-based controller cannot provide valid control signals.
  arXiv  Detail & Related papers  (2023-04-08T16:48:49Z)
• Meta-Learning Priors for Safe Bayesian Optimization [72.8349503901712]
  We build on a meta-learning algorithm, F-PACOH, capable of providing reliable uncertainty quantification in settings of data scarcity. As core contribution, we develop a novel framework for choosing safety-compliant priors in a data-riven manner. On benchmark functions and a high-precision motion system, we demonstrate that our meta-learned priors accelerate the convergence of safe BO approaches.
  arXiv  Detail & Related papers  (2022-10-03T08:38:38Z)
• Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments [84.3830478851369]
  We propose a safe reinforcement learning approach that can jointly learn the environment and optimize the control policy. Our approach can effectively enforce hard safety constraints and significantly outperform CMDP-based baseline methods in system safe rate measured via simulations.
  arXiv  Detail & Related papers  (2022-09-29T20:49:25Z)
• Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions [60.26921219698514]
  We introduce a model-uncertainty-aware reformulation of CBF-based safety-critical controllers. We then present the pointwise feasibility conditions of the resulting safety controller. We use these conditions to devise an event-triggered online data collection strategy.
  arXiv  Detail & Related papers  (2022-08-23T05:02:09Z)
• Pointwise Feasibility of Gaussian Process-based Safety-Critical Control under Model Uncertainty [77.18483084440182]
  Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs) are popular tools for enforcing safety and stability of a controlled system, respectively. We present a Gaussian Process (GP)-based approach to tackle the problem of model uncertainty in safety-critical controllers that use CBFs and CLFs.
  arXiv  Detail & Related papers  (2021-06-13T23:08:49Z)
• Learning Hybrid Control Barrier Functions from Data [66.37785052099423]
  Motivated by the lack of systematic tools to obtain safe control laws for hybrid systems, we propose an optimization-based framework for learning certifiably safe control laws from data. In particular, we assume a setting in which the system dynamics are known and in which data exhibiting safe system behavior is available.
  arXiv  Detail & Related papers  (2020-11-08T23:55:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.