mlsanityData to 7 Oct 2026

Harmonic Control Lyapunov Barrier Functions for Constrained Optimal Control with Reach-Avoid Specifications

arXiv 2310.02869 ↗First posted 4 Oct 2023math.OC cs.LG math.APcited by 0

Not on the map. Why?

Abstract (arXiv)

This paper introduces harmonic control Lyapunov barrier functions (harmonic CLBF) that aid in constrained control problems such as reach-avoid problems. Harmonic CLBFs exploit the maximum principle that harmonic functions satisfy to encode the properties of control Lyapunov barrier functions (CLBFs). As a result, they can be initiated at the start of an experiment rather than trained based on sample trajectories. The control inputs are selected to maximize the inner product of the system dynamics with the steepest descent direction of the harmonic CLBF. Numerical results are presented with four different systems under different reach-avoid environments. Harmonic CLBFs show a significantly low risk of entering unsafe regions and a high probability of entering the goal region.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 21 references that are in the map. First posted on arXiv 4 Oct 2023.

Builds on

9 of 21 references.

References · 9

  1. 2015Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decompositioncited by 241
  2. 2017Emergence of Locomotion Behaviours in Rich Environmentscited by 159
  3. 2021Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functionscited by 91
  4. 2020Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functionscited by 61
  5. 2021safe-control-gym: a Unified Benchmark Suite for Safe Learning-based Control and Reinforcement Learning in Roboticscited by 42
  6. 2016Gradient Descent Converges to Minimizerscited by 25
  7. 2023Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functionscited by 9
  8. 2020Smooth Converse Lyapunov-Barrier Theorems for Asymptotic Stability with Safety Constraints and Reach-Avoid-Stay Specificationscited by 4
  9. 2023Provable Reset-free Reinforcement Learning by No-Regret Reductioncited by 3

Built on by

Not cited yet.