ResearchResearch paperReinforcement Learning1 source · Oct 12, 2026

Reach-Avoid Certificates for Contact-based Stochastic Hybrid Systems

This work studies certified reach-avoid control of stochastic hybrid systems in which a stochastic differential equation governs continuous flow and a controlled stochastic kernel governs guard-triggered resets.

Key points

  • Certificates for such systems have so far assumed spontaneous or controller-chosen jump times, a finite horizon, or discrete-time dynamics; we certify, over the infinite horizon, that the target is reached before the unsafe set with at least a prescribed probability.
  • First, we introduce reach-avoid contact certificates, whose flow and jump conditions are imposed only on a sublevel corridor, and prove that any such certificate with a compatible reset policy meets the specification; a pointwise safety filter extends this guarantee from verification to control by constraining arbitrary nominal controllers.
  • We frame certificate search as a linear program over any twice-differentiable dictionary followed by independent verification.
  • We illustrate our approach via comparisons where the certified region is a subset covering 93.6% of the ground truth and where our certificate-based safety filter reaches the goal while reducing collisions from 68 to zero across 200 episodes of a dribbling ball problem.

Sources (1)

  • [1]Reach-Avoid Certificates for Contact-based Stochastic Hybrid Systems
    arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 07:04 PM
    This work studies certified reach-avoid control of stochastic hybrid systems in which a stochastic differential equation governs continuous flow and a controlled stochastic kernel governs guard-triggered resets.
    Certificates for such systems have so far assumed spontaneous or controller-chosen jump times, a finite horizon, or discrete-time dynamics; we certify, over the infinite horizon, that the target is reached before the unsafe set with at least a prescribed probability.

Extractive summary: sentences quoted from the sources.

Before this

  1. Oct 12, 2026DuoDiscover: Scaling Autonomous Discovery Beyond the Evaluation Bottleneck
  2. Oct 11, 2026Heterogeneous Structured Pruning Under Resource Constraints for Hardware-Efficient Large Language Models
  3. Oct 11, 2026REPRO: Reflective Evolutionary Prompt Optimizer for Automated LLM Red-Teaming
  4. Oct 11, 2026SpecFit: Not All Draft Tokens Cost the Same in Offloaded MoE Inference
  5. Oct 11, 2026Imperfect Verifiers, Exact Targets: Inference-Time Scaling for Continuous Generation
  6. Oct 10, 2026How Should a Camera-First AI Agent Work?

Related