{"ID":5937914,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-07T08:05:00.133216355Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.03758","arxiv_id":"2607.03758","title":"Occluding the Solution Space: Planner-Agnostic Adversarial Attacks on Tolerance-Aware Manipulation","abstract":"Adversarial attacks on motion planning are crucial for evaluating and quantifying the intrinsic robustness of robotic manipulation. However, existing approaches are typically limited by restrictive exact-pose objectives and their reliance on planner-in-the-loop queries. To address these limitations, we propose a planner-agnostic attack framework for tolerance-aware manipulation. Our approach shifts the evaluation paradigm to task-level feasibility over goal regions, efficiently inserting adversarial obstacles without requiring oracle access to the victim system. Offline, we characterize the robot's intrinsic workspace capabilities via a kinematic occupancy heatmap, which encodes the density of feasible trajectories and robustness priors without invoking a specific planner. Online, we formulate the attack as a budgeted maximum-coverage optimization, strategically deploying obstacles subject to explicit geometric constraints to occlude the solution space. Extensive experiments across simulation and real-world scenarios demonstrate that our method reliably induces planning failures, significantly outperforming planner-in-the-loop baselines in both computational efficiency and attack efficacy.","short_abstract":"Adversarial attacks on motion planning are crucial for evaluating and quantifying the intrinsic robustness of robotic manipulation. However, existing approaches are typically limited by restrictive exact-pose objectives and their reliance on planner-in-the-loop queries. To address these limitations, we propose a planne...","url_abs":"https://arxiv.org/abs/2607.03758","url_pdf":"https://arxiv.org/pdf/2607.03758v1","authors":"[\"Keke Tang\",\"Tianyu Hao\",\"Weilong Peng\",\"Hao Jiang\",\"Feng Wu\",\"Peican Zhu\",\"Jianmin Ji\",\"Zhihong Tian\"]","published":"2026-07-04T08:14:44Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.CR\"]","methods":"[]","has_code":false}