{"ID":2862895,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.26459","arxiv_id":"2509.26459","title":"Analytic Conditions for Differentiable Collision Detection in Trajectory Optimization","abstract":"Optimization-based methods are widely used for computing fast, diverse solutions for complex tasks such as collision-free movement or planning in the presence of contacts. However, most of these methods require enforcing non-penetration constraints between objects, resulting in a non-trivial and computationally expensive problem. This makes the use of optimization-based methods for planning and control challenging. In this paper, we present a method to efficiently enforce non-penetration of sets while performing optimization over their configuration, which is directly applicable to problems like collision-aware trajectory optimization. We introduce novel differentiable conditions with analytic expressions to achieve this. To enforce non-collision between non-smooth bodies using these conditions, we introduce a method to approximate polytopes as smooth semi-algebraic sets. We present several numerical experiments to demonstrate the performance of the proposed method and compare the performance with other baseline methods recently proposed in the literature.","short_abstract":"Optimization-based methods are widely used for computing fast, diverse solutions for complex tasks such as collision-free movement or planning in the presence of contacts. However, most of these methods require enforcing non-penetration constraints between objects, resulting in a non-trivial and computationally expensi...","url_abs":"https://arxiv.org/abs/2509.26459","url_pdf":"https://arxiv.org/pdf/2509.26459v1","authors":"[\"Akshay Jaitly\",\"Devesh K. Jha\",\"Kei Ota\",\"Yuki Shirai\"]","published":"2025-09-30T16:09:52Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.CG\"]","methods":"[]","has_code":false}