Online, Reachability-Aware, Sampling-Based Motion Planning

• URL: http://arxiv.org/abs/2609.09073v1
• Date: Tue, 08 Sep 2026 17:27:25 GMT
• Title: Online, Reachability-Aware, Sampling-Based Motion Planning
• Abstract summary: We show that our method achieves similar performance to a state-of-the-art reachability-based planner without the need for the expensive pre-computation step.<n>We demonstrate that our technique reduces safety violations by over 99% in a racing simulation and successfully controls a model racecar on real hardware experiments without crashes.
• Score: 15.579525714267158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sampling-Based Model-Predictive Control (MPC) algorithms are a flexible class of controllers used for navigation on a wide range of robotic systems. Historically, such approaches have lacked hard safety guarantees, a shortcoming which we remedy in this work by computing guaranteed reachable-set overapproximations online with a fast, interval-based pipeline. We show that our method achieves similar performance to a state-of-the-art reachability-based planner without the need for the expensive pre-computation step, and can be scaled to systems that are infeasible using existing approaches. Finally, we demonstrate that our technique reduces safety violations by over 99% in a racing simulation and successfully controls a model racecar on real hardware experiments without crashes.

Related papers

• Designing Control Barrier Function via Probabilistic Enumeration for Safe Reinforcement Learning Navigation [55.02966123945644]
  We propose a hierarchical control framework leveraging neural network verification techniques to design control barrier functions (CBFs) and policy correction mechanisms.<n>Our approach relies on probabilistic enumeration to identify unsafe regions of operation, which are then used to construct a safe CBF-based control layer.<n>These experiments demonstrate the ability of the proposed solution to correct unsafe actions while preserving efficient navigation behavior.
  arXiv  Detail & Related papers  (2025-04-30T13:47:25Z)
• Learning Vision-Based Neural Network Controllers with Semi-Probabilistic Safety Guarantees [24.650302053973142]
  We introduce a novel semi-probabilistic verification framework that integrates reachability analysis with conditional generative adversarial networks.<n>Next, we develop a gradient-based training approach that employs a novel safety loss function, safety-aware data-sampling strategy, and curriculum learning.<n> Empirical evaluations in X-Plane 11 airplane landing simulation, CARLA-simulated autonomous lane following, and F1Tenth lane following in a visually-rich miniature environment demonstrate the effectiveness of our method in achieving formal safety guarantees.
  arXiv  Detail & Related papers  (2025-02-28T21:16:42Z)
• Estimating Control Barriers from Offline Data [14.241303913878887]
  We propose a novel framework for learning neural CBFs through a fixed, sparsely-labeled dataset collected prior to training.<n>With limited amount of offline data, it achieves state-of-the-art performance for dynamic obstacle avoidance.
  arXiv  Detail & Related papers  (2025-02-21T04:55:20Z)
• Tuning Legged Locomotion Controllers via Safe Bayesian Optimization [47.87675010450171]
  This paper presents a data-driven strategy to streamline the deployment of model-based controllers in legged robotic hardware platforms. We leverage a model-free safe learning algorithm to automate the tuning of control gains, addressing the mismatch between the simplified model used in the control formulation and the real system.
  arXiv  Detail & Related papers  (2023-06-12T13:10:14Z)
• In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution States [84.24300005271185]
  We propose a control filter that wraps any reference policy and effectively encourages the system to stay in-distribution with respect to offline-collected safe demonstrations. Our method is effective for two different visuomotor control tasks in simulation environments, including both top-down and egocentric view settings.
  arXiv  Detail & Related papers  (2023-01-27T22:28:19Z)
• 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)
• Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning [72.97229770329214]
  We introduce a general approach for seeking high dimensional non-linear optimization problems in which maintaining safety during learning is crucial. Our approach called LBSGD is based on applying a logarithmic barrier approximation with a carefully chosen step size. We demonstrate the effectiveness of our approach on minimizing violation in policy tasks in safe reinforcement learning.
  arXiv  Detail & Related papers  (2022-07-21T11:14:47Z)
• Networked Online Learning for Control of Safety-Critical Resource-Constrained Systems based on Gaussian Processes [9.544146562919792]
  We propose a novel networked online learning approach based on Gaussian process regression. We propose an effective data transmission scheme between the local system and the cloud taking bandwidth limitations and time delay of the transmission channel into account.
  arXiv  Detail & Related papers  (2022-02-23T13:12:12Z)
• Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems [81.7983463275447]
  Learning-based control algorithms require data collection with abundant supervision for training. We present a new approach for optimal motion planning with safe exploration that integrates chance-constrained optimal control with dynamics learning and feedback control.
  arXiv  Detail & Related papers  (2020-05-09T05:57:43Z)

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.