Learned Lyapunov Shielding for Adaptive Control

• URL: http://arxiv.org/abs/2605.06934v1
• Date: Thu, 07 May 2026 20:49:22 GMT
• Title: Learned Lyapunov Shielding for Adaptive Control
• Abstract summary: We augment the Slotine--Li adaptive controller for Euler--Lagrange systems with three learned components.<n>A closed-form safety filter projects every policy output onto the safe set without requiring an online QP solver.<n>A 7-DOF scalability study on a Franka Emika Panda confirms clean convergence of the full pipeline at industrial scale.
• Score: 1.2891210250935148
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We augment the Slotine--Li adaptive controller for Euler--Lagrange systems with three learned components: a structured-quadratic Lyapunov function \(V_ψ\) whose positive-definiteness follows from a Cholesky parameterization, a residual Soft Actor--Critic policy that adds bounded torque corrections to the analytic baseline, and a physics-informed neural network that estimates unmodeled dynamics. A closed-form safety filter, derived from the single affine constraint \(\dot V_ψ+ αV_ψ\le 0\), projects every policy output onto the safe set without requiring an online QP solver. We prove: global feasibility of the filter under a drift-decay condition on the control-degeneracy set; exponential stability under exact shielding, with a robust extension whose margin depends on the PINN approximation error; almost-sure convergence of the three-timescale policy--certificate--multiplier updates to a KKT point; and a PAC generalization bound for the certificate over compacts. On a 2-DOF manipulator with nonlinear friction and variable payload, the learned certificate accounts for most of the empirical gain: tracking error drops by 41\% on nominal friction and 24\% on aggressive friction at the centroid of the training distribution. A 7-DOF scalability study on a Franka Emika Panda confirms clean convergence of the full pipeline at industrial scale, identifies the conditions under which gains over exact model-based baselines should and should not be expected, and documents a warm-start pathology of the learned certificate that has practical implications for deployment.

Related papers

• Finite Reliability Representations: Noise-Calibrated Belief-Space Covers for Reliable Decision-Making [0.0]
  We introduce Finite Reliability Representations (FRR), a framework for covering belief spaces by reliability cells.<n>FRR is formulated on beliefs rather than states and uses a cover rather than an equivalence quotient.<n>It applies to finite POMDPs, linear-Gaussian filters, locally linearized nonlinear filters, and particle-filter implementations.
  arXiv  Detail & Related papers  (2026-07-04T20:30:48Z)
• Distribution-Aware Robust Bilevel Optimization: Quantile-Guided Huber Updates in Two-Timescale Stochastic Approximation [16.106678611834102]
  Bilevel optimization (BLO) is fundamental to hierarchical decision-making but suffers from critical instability under heavy-tailed noise.<n>Existing techniques typically rely on magnitude checks, which fail to distinguish informative geometric signals from outliers.
  arXiv  Detail & Related papers  (2026-06-21T10:57:37Z)
• Stable Transformer-Actor-Critic Model Predictive Control: A Contraction Analysis Approach [1.7625675658723843]
  Actor-Critic Predictive Control (MPC) effectively addresses complex, non-reaching control problems.<n>We introduce a novel Transformer-Actor-Critic MPC architecture with formal robustness architecture.
  arXiv  Detail & Related papers  (2026-06-18T13:11:21Z)
• Beyond the Attention Stability Boundary: Agentic Self-Synthesizing Reasoning Protocols [6.357772907811544]
  Self- Synthesizing Reasoning Protocols (SSRP) is a metacognitive framework that implements a discrete separation between architectural planning and procedural execution.<n>We present 3 experimental tiers: a shallow recency-based retrieval pilot, a high-entropy SOP, and a semantic hijacked 3-hop Multi-Fact Synthesis task.<n>Our results empirically locate the Attention Stability Boundary, where stateless Vanilla ReAct baselines for GPT 5.4 collapse to 0.1% success while SSRP achieves a 715X Resilience Lift.
  arXiv  Detail & Related papers  (2026-04-27T14:13:30Z)
• Response-Aware Risk-Constrained Control Barrier Function With Application to Vehicles [0.0]
  This paper proposes a unified control framework based on Response-Aware Risk-Constrained Control Barrier Function for dynamic safety boundary control of vehicles.<n>The framework fuses nominal dynamics priors with direct vehicle body responses to provide a baseline direction for control gradients.<n>It also reformulates traditional deterministic safety constraints into probabilistic constraints on the tail risk of barrier function derivatives.
  arXiv  Detail & Related papers  (2026-03-13T02:38:17Z)
• Improving Search Agent with One Line of Code [68.58667107354253]
  Tool-based Agentic Reinforcement Learning (TARL) has emerged as a promising paradigm for training search agents to interact with external tools.<n>We propose textbfSearch textbfAgent textbfPolicy textbfOptimization (textbfSAPO), which stabilizes training via a conditional token-level KL constraint.
  arXiv  Detail & Related papers  (2026-03-10T04:07:39Z)
• BandPO: Bridging Trust Regions and Ratio Clipping via Probability-Aware Bounds for LLM Reinforcement Learning [49.25750348525603]
  BandPO replaces canonical clipping with Band, a unified theoretical operator that projects trust regions into dynamic, probability-aware clipping intervals.<n>We show that BandPO consistently outperforms canonical clipping and Clip-Higher, while robustly mitigating entropy collapse.
  arXiv  Detail & Related papers  (2026-03-05T08:03:05Z)
• Safe Urban Traffic Control via Uncertainty-Aware Conformal Prediction and World-Model Reinforcement Learning [43.06827300023392]
  STREAM-RL is an Uncertainty-Guided Adaptive Conformal Forecaster, a Conformal Residual Flow Network, and an Uncertainty-Guided Safe World-Model RL agent.<n>Experiments on multiple real-world traffic trajectory data demonstrate that STREAM-RL achieves 91.4% coverage efficiency, controls FDR at 4.1% under verified dependence, and improves safety rate to 95.2% compared to 69% for standard PPO.
  arXiv  Detail & Related papers  (2026-02-04T18:10:59Z)
• How to Train Your Latent Control Barrier Function: Smooth Safety Filtering Under Hard-to-Model Constraints [21.03977709777739]
  We show that reachability value functions can be adapted to be control barrier functions (CBFs) for smooth optimization-based filtering.<n>We propose the LatentCBF, which addresses both challenges through gradient penalties that lead to smooth margin functions without additional labeling.<n>Experiments on simulated benchmarks and hardware with a vision-based manipulation policy demonstrate that LatentCBF enables smooth safety filtering.
  arXiv  Detail & Related papers  (2025-11-23T20:15:28Z)
• Continual Action Quality Assessment via Adaptive Manifold-Aligned Graph Regularization [53.82400605816587]
  Action Quality Assessment (AQA) quantifies human actions in videos, supporting applications in sports scoring, rehabilitation, and skill evaluation.<n>A major challenge lies in the non-stationary nature of quality distributions in real-world scenarios.<n>We introduce Continual AQA (CAQA), which equips with Continual Learning capabilities to handle evolving distributions.
  arXiv  Detail & Related papers  (2025-10-08T10:09:47Z)
• Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty [49.19257648205146]
  We propose an unsupervised conformal inference framework for generation.<n>Our gates achieve close-to-nominal coverage and provide tighter, more stable thresholds than split UCP.<n>The result is a label-free, API-compatible gate for test-time filtering.
  arXiv  Detail & Related papers  (2025-09-26T23:40:47Z)
• Distributionally Robust Safety Verification of Neural Networks via Worst-Case CVaR [3.0458514384586404]
  This paper builds on Fazlyab's quadratic-constraint (QC) and semidefinite-programming (SDP) framework for neural network verification.<n>The integration broadens input-uncertainty geometry-covering ellipsoids, polytopes, and hyperplanes-and extends applicability to safety-critical domains.
  arXiv  Detail & Related papers  (2025-09-22T07:04:53Z)
• 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)
• Robust Implicit Networks via Non-Euclidean Contractions [63.91638306025768]
  Implicit neural networks show improved accuracy and significant reduction in memory consumption. They can suffer from ill-posedness and convergence instability. This paper provides a new framework to design well-posed and robust implicit neural networks.
  arXiv  Detail & Related papers  (2021-06-06T18:05:02Z)

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.