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Computer Science > Robotics

arXiv:2409.13195 (cs)
[Submitted on 20 Sep 2024 (v1), last revised 3 Mar 2025 (this version, v2)]

Title:Guaranteed Reach-Avoid for Black-Box Systems through Narrow Gaps via Neural Network Reachability

Authors:Long Kiu Chung, Wonsuhk Jung, Srivatsank Pullabhotla, Parth Shinde, Yadu Sunil, Saihari Kota, Luis Felipe Wolf Batista, Cédric Pradalier, Shreyas Kousik
View a PDF of the paper titled Guaranteed Reach-Avoid for Black-Box Systems through Narrow Gaps via Neural Network Reachability, by Long Kiu Chung and 8 other authors
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Abstract:In the classical reach-avoid problem, autonomous mobile robots are tasked to reach a goal while avoiding obstacles. However, it is difficult to provide guarantees on the robot's performance when the obstacles form a narrow gap and the robot is a black-box (i.e. the dynamics are not known analytically, but interacting with the system is cheap). To address this challenge, this paper presents NeuralPARC. The method extends the authors' prior Piecewise Affine Reach-avoid Computation (PARC) method to systems modeled by rectified linear unit (ReLU) neural networks, which are trained to represent parameterized trajectory data demonstrated by the robot. NeuralPARC computes the reachable set of the network while accounting for modeling error, and returns a set of states and parameters with which the black-box system is guaranteed to reach the goal and avoid obstacles. NeuralPARC is shown to outperform PARC, generating provably-safe extreme vehicle drift parking maneuvers in simulations and in real life on a model car, as well as enabling safety on an autonomous surface vehicle (ASV) subjected to large disturbances and controlled by a deep reinforcement learning (RL) policy.
Comments: This work has been submitted for possible publication
Subjects: Robotics (cs.RO); Systems and Control (eess.SY)
Cite as: arXiv:2409.13195 [cs.RO]
  (or arXiv:2409.13195v2 [cs.RO] for this version)
  https://doi.org/10.48550/arXiv.2409.13195
arXiv-issued DOI via DataCite

Submission history

From: Long Kiu Chung [view email]
[v1] Fri, 20 Sep 2024 03:55:47 UTC (2,397 KB)
[v2] Mon, 3 Mar 2025 16:38:51 UTC (2,995 KB)
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