TriSearch: Learning to Optimize Triangulations via Bistellar Flips
On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation
Abstract (arXiv)
We introduce TriSearch, a reinforcement learning framework for optimizing objectives over triangulations of a polytope via bistellar flips. The key idea is a circuit-supported subtriangulation action representation: feasible flips are encoded by their supporting circuit and realized local subtriangulation, enabling a learned policy to rank them using local geometric and combinatorial features. This yields a dimension-agnostic interface and enables efficient traversal of the flip graph without explicit enumeration of the full triangulation space. Instantiated in 3D and 4D, TriSearch generalizes zero-shot from small training instances to larger polytopes with exponentially larger search spaces. It achieves top performance on metric objectives in 3D and, in 4D, discovers more distinct Fine, Regular, Star triangulations of reflexive polytopes, corresponding to Calabi-Yau threefolds, than existing samplers under a fixed budget.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 23 of its 48 references that are in the map. First posted on arXiv 28 May 2026.
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23 of 48 references.
References · 23
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11 more
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- 2021Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes
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