{"ID":2886347,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.03428","arxiv_id":"2508.03428","title":"Residual Neural Terminal Constraint for MPC-based Collision Avoidance in Dynamic Environments","abstract":"In this paper, we propose a hybrid MPC local planner that uses a learning-based approximation of a time-varying safe set, derived from local observations and applied as the MPC terminal constraint. This set can be represented as a zero-superlevel set of the value function computed via Hamilton-Jacobi (HJ) reachability analysis, which is infeasible in real-time. We exploit the property that the HJ value function can be expressed as a difference of the corresponding signed distance function (SDF) and a non-negative residual function. The residual component is modeled as a neural network with non-negative output and subtracted from the computed SDF, resulting in a real-time value function estimate that is at least as safe as the SDF by design. Additionally, we parametrize the neural residual by a hypernetwork to improve real-time performance and generalization properties. The proposed method is compared with three state-of-the-art methods in simulations and hardware experiments, achieving up to 30\\% higher success rates compared to the best baseline while requiring a similar computational effort and producing high-quality (low travel-time) solutions.","short_abstract":"In this paper, we propose a hybrid MPC local planner that uses a learning-based approximation of a time-varying safe set, derived from local observations and applied as the MPC terminal constraint. This set can be represented as a zero-superlevel set of the value function computed via Hamilton-Jacobi (HJ) reachability...","url_abs":"https://arxiv.org/abs/2508.03428","url_pdf":"https://arxiv.org/pdf/2508.03428v2","authors":"[\"Bojan Derajić\",\"Mohamed-Khalil Bouzidi\",\"Sebastian Bernhard\",\"Wolfgang Hönig\"]","published":"2025-08-05T13:17:13Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.LG\",\"eess.SY\"]","methods":"[]","has_code":false}