Convex Collision-Free Regions
- URL: http://arxiv.org/abs/2607.26901v1
- Date: Wed, 29 Jul 2026 13:33:37 GMT
- Title: Convex Collision-Free Regions
- Abstract summary: Convex Collision-Free Regions (CCFR) is a collision handling method that explicitly represents local convex feasible regions to enforce non-penetration.<n>CCFR does not rely on nonlinear optimization and is compatible with simulation frameworks such as Extended Position-Based Dynamics (XPBD)<n>The effectiveness of CCFR is demonstrated across cloth, hair, wire, particle systems, and codimensional contact scenarios.
- Score: 1.503974529275767
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Convex Collision-Free Regions (CCFR) is a collision handling method that explicitly represents local convex feasible regions to enforce non-penetration. Each feasible region is constructed from surrounding mesh primitive configurations, including edge-edge and vertex-face interactions. The resulting convex region represents admissible non-penetrating vertex displacements at the current configuration. Existing collision handling methods for deformable body simulation have largely relied on implicit representations of feasibility, resulting in either compromised robustness for secondary collisions and codimensional contacts or tight coupling with specific nonlinear optimization schemes. Our formulation constructs feasible regions independently for each vertex, defined prior to penetration, inherently accounts not only for primary collisions but also for secondary collisions and codimensional contacts, enabling highly scalable and parallelizable collision handling. These feasible regions encode geometric non-penetration constraints independently of physical contact response models. CCFR does not rely on nonlinear optimization and is compatible with simulation frameworks such as Extended Position-Based Dynamics (XPBD) that do not explicitly maintain interior feasibility during iterative updates. The effectiveness of CCFR is demonstrated across cloth, hair, wire, particle systems, and codimensional contact scenarios, showing versatile and efficient collision handling.
Related papers
- Control Barrier Functions via Minkowski Operations for Safe Navigation among Polytopes [6.281375484813414]
We propose an exact Signed Distance Function (SDF) formulation for a it polytopic robot and it polytopic obstacles.<n>The proposed method computes the exact SDF via companion convex programs in both the collision-free (positive-sign) and in-collision (negative-sign) cases.
arXiv Detail & Related papers (2026-08-03T21:15:23Z) - CCFM: Collision-Constrained Flow Matching for Safety-Critical Scenario Generation [12.01753684349527]
We introduce Collision-Constrained Flow Matching (CCFM), a novel framework that guarantees precise collision control through hard physical constraints.<n>CCFM collision rate up to 46.4% on nuScenes and 83.1% on nuPlan, significantly outperforming baselines while preserving realistic driving behavior.
arXiv Detail & Related papers (2026-07-05T18:26:14Z) - Learning Unions of Convex Sets via Invertible Latent Decomposition for Path Planning [19.14344979340464]
Invertible Latent Decomposition (ILD) is a framework that learns an invertible mapping and a union of explicit convex polytopes in the resulting latent space.<n>ILD achieves broader coverage, better inter-set connectivity, and higher path-planning success rates than prior baselines.<n>On a 14-DoF bimanual manipulator, we demonstrate real-time collision-free planning, with test-time refinement adapting to scene-geometry changes.
arXiv Detail & Related papers (2026-06-10T12:49:47Z) - OcclusionFormer: Arranging Z-Order for Layout-Grounded Image Generation [49.49933361280724]
We introduce OcclusionFormer, a novel framework that explicitly models Z-order priority by decoupling instances.<n>We also introduce a queried alignment loss that explicitly supervises individual instances and enhances semantic consistency.<n>The proposed method effectively reduces ambiguity in overlapping regions, enforces correct occlusion dependencies, and preserves structural integrity, leading to substantial accuracy gains across diverse scenes.
arXiv Detail & Related papers (2026-05-20T16:10:44Z) - Adaptive Smooth Tchebycheff Attention for Multi-Objective Policy Optimization [55.338784037660474]
Multi- curvature reinforcement learning in domains requires balancing complex, non-fragile interference trade-offs between conflicting objectives.<n>In this paper we introduce a novel based real-time based real-time optimization on a robotic visual -- a proxy for monitoring ecosystems.
arXiv Detail & Related papers (2026-05-12T21:32:57Z) - CSSDF-Net: Safe Motion Planning Based on Neural Implicit Representations of Configuration Space Distance Field [23.48697826890044]
High-dimensional manipulator operation in unstructured environments requires differentiable, scene-agnostic distance query mechanism.<n>CSSDF-Net learns a continuous signed distance field directly in configuration space to provide joint-space distance and gradient queries.<n> Experiments on a planar arm and a 7-DoF manipulator demonstrate stable gradients, effective collision avoidance in static and dynamic scenes.
arXiv Detail & Related papers (2026-03-19T09:31:41Z) - Collision Detection with Analytical Derivatives of Contact Kinematics [2.9976270169593495]
Differentiable contact kinematics are essential for gradient-based methods in robotics.<n>We develop iDCOL, an implicit differentiable collision detection and contact kinematics framework.
arXiv Detail & Related papers (2026-02-03T08:34:31Z) - Fast and Safe Trajectory Optimization for Mobile Manipulators With Neural Configuration Space Distance Field [34.07401865897874]
Generalized Space Fields (GCDF) can be used to extend base-arm coupling to mobile manipulators.<n>GCDF encodes whole-body configuration in configuration in unbounded space.<n>We develop a high-performance optimization solver centered on GCDF-based reasoning.
arXiv Detail & Related papers (2026-01-26T14:55:26Z) - Steerable Adversarial Scenario Generation through Test-Time Preference Alignment [58.37104890690234]
Adversarial scenario generation is a cost-effective approach for safety assessment of autonomous driving systems.<n>We introduce a new framework named textbfSteerable textbfAdversarial scenario textbfGEnerator (SAGE)<n>SAGE enables fine-grained test-time control over the trade-off between adversariality and realism without any retraining.
arXiv Detail & Related papers (2025-09-24T13:27:35Z) - Fast Gravitational Approach for Rigid Point Set Registration with
Ordinary Differential Equations [79.71184760864507]
This article introduces a new physics-based method for rigid point set alignment called Fast Gravitational Approach (FGA)
In FGA, the source and target point sets are interpreted as rigid particle swarms with masses interacting in a globally multiply-linked manner while moving in a simulated gravitational force field.
We show that the new method class has characteristics not found in previous alignment methods.
arXiv Detail & Related papers (2020-09-28T15:05:39Z) - Scalable Differentiable Physics for Learning and Control [99.4302215142673]
Differentiable physics is a powerful approach to learning and control problems that involve physical objects and environments.
We develop a scalable framework for differentiable physics that can support a large number of objects and their interactions.
arXiv Detail & Related papers (2020-07-04T19:07:51Z) - Dense Non-Rigid Structure from Motion: A Manifold Viewpoint [162.88686222340962]
Non-Rigid Structure-from-Motion (NRSfM) problem aims to recover 3D geometry of a deforming object from its 2D feature correspondences across multiple frames.
We show that our approach significantly improves accuracy, scalability, and robustness against noise.
arXiv Detail & Related papers (2020-06-15T09:15:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.