用高斯场景表示实现机器人实时避障与动态控制
SplatCtrl: Perception-Action Coupling via Gaussian Scene Representations and Reactive Robot Control
- 基于3D高斯点云融合视觉与深度数据，实时重建环境
- 通过连续距离场估计，实现稳定可微的碰撞概率计算
- 端到端耦合感知与动作，在动态环境中保持安全运动
机器人机械臂在结构化环境中表现优异，但在非结构化和动态环境中面临巨大挑战。本文提出SplatCtrl，一个统一框架，实现实时场景重建与反应式机器人运动生成，支持在未知且持续变化的环境中实现无碰撞机械臂控制。基于3D高斯点云（3D-GS），我们引入混合体素过滤与动态高斯重定位策略，从RGB-D流中高效重建场景并适应环境变化。为实现安全、反应式的控制，我们进一步提出从各向同性高斯中推导连续符号距离函数的方法，提供稳定且可微的碰撞概率估计，将经典距离场与现代隐式表示相衔接。这些连续距离度量被集成到控制屏障函数中，构建了一个统一的感知-动作耦合框架，可在场景变化下实现平滑可靠的实时运动生成。仿真、物理机器人及人机共享工作空间中的实验验证了该框架的有效性，实现了在不确定、动态环境中的集成重建与反应式控制。
原文摘要 · Abstract (English)
Robotic manipulators excel in structured environments but face substantial challenges in unstructured and dynamic settings. This paper presents SplatCtrl, a unified framework for real-time scene reconstruction and reactive robot motion generation to enable collision-free robotic arm control in previously unseen and continuously changing environments. Building on 3D Gaussian Splatting (3D-GS), we introduce a hybrid voxel-based filtering and dynamic Gaussian relocation strategy that supports efficient scene reconstruction from RGB-D streams while accommodating environmental changes. For safe and reactive control, we further propose a method for deriving continuous signed distance functions from isotropic Gaussians, providing stable and differentiable collision probability estimates that bridge classical distance fields with the modern implicit representation. These continuous distance metrics are incorporated into control barrier functions, resulting in a unified perception-action coupling framework that supports smooth and reliable real-time motion generation in response to scene changes. Experimental validation in simulation, on physical robot, and within shared human-robot workspace demonstrates the framework's effectiveness, achieving integrated scene reconstruction and reactive control in uncertain, and dynamic environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。