{"ID":2898671,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.02438","arxiv_id":"2507.02438","title":"Minimal Intervention Shared Control with Guaranteed Safety under Non-Convex Constraints","abstract":"Shared control combines human intention with autonomous decision-making. At the low level, the primary goal is to maintain safety regardless of the user's input to the system. However, existing shared control methods-based on, e.g., Model Predictive Control, Control Barrier Functions, or learning-based control-often face challenges with feasibility, scalability, and mixed constraints. To address these challenges, we propose a Constraint-Aware Assistive Controller that computes control actions online while ensuring recursive feasibility, strict constraint satisfaction, and minimal deviation from the user's intent. It also accommodates a structured class of non-convex constraints common in real-world settings. We leverage Robust Controlled Invariant Sets for recursive feasibility and a Mixed-Integer Quadratic Programming formulation to handle non-convex constraints. We validate the approach through a large-scale user study with 66 participants-one of the most extensive in shared control research-using a simulated environment to assess task load, trust, and perceived control, in addition to performance. The results show consistent improvements across all these aspects without compromising safety and user intent. Additionally, a real-world experiment on a robotic manipulator demonstrates the framework's applicability under bounded disturbances, ensuring safety and collision-free operation.","short_abstract":"Shared control combines human intention with autonomous decision-making. At the low level, the primary goal is to maintain safety regardless of the user's input to the system. However, existing shared control methods-based on, e.g., Model Predictive Control, Control Barrier Functions, or learning-based control-often fa...","url_abs":"https://arxiv.org/abs/2507.02438","url_pdf":"https://arxiv.org/pdf/2507.02438v2","authors":"[\"Shivam Chaubey\",\"Francesco Verdoja\",\"Shankar Deka\",\"Ville Kyrki\"]","published":"2025-07-03T08:52:05Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.HC\",\"eess.SY\"]","methods":"[]","has_code":false}