3D点云世界模型：点云补全实现更准确的动力学学习
3D Point World Models: Point Completion Enables More Accurate Dynamics Learning
- Oregon State University（俄勒冈州立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出3D点云世界模型(3DPWM)，通过先补全部分点云再学习动作条件动力学，实现长时程可靠推演和精确成本评估，支持多种机器人操作任务。
AI中文摘要：
学习世界预测模型能够通过规划实现机器人控制，使机器人能够为新任务即兴提出解决方案。然而，基于视频的大规模动力学模型缺乏显式的3D空间结构，且长期推演中由于误差累积导致几何不一致。基于部分点云的3D动力学模型虽改善了几何一致性，但仍对遮挡和累积预测漂移敏感。为解决这些问题，我们提出3D点云世界模型(3DPWM)——一种任务无关的世界模型，它完全在3D空间中运作，首先补全部分点云，然后在此补全的3D场景中学习动作条件动力学。通过基于补全几何的操作，3DPWM能够实现可靠的长期推演和更准确的基于模型的规划成本评估，同时支持对新任务的适应。在不同机器人实体和桌面操作基准上的实验表明，3DPWM实现了显著更可靠的长期推演（100-300+步），支持开环和闭环规划，并实现了成功的仿真到现实迁移。
英文摘要：
Learning predictive models of the world enables robotic control through planning, potentially allowing robots to improvise solutions on new tasks. However, large video-based dynamics models lack explicit 3D spatial structure and suffer from geometrically inconsistent long-term rollouts with compounding errors. Emerging 3D dynamics models based on partial point clouds improve geometric consistency but remain sensitive to occlusions and accumulated prediction drift. To address these challenges, we present 3D Point World Models (3DPWM) - a task-agnostic world model that operates entirely in 3D space by first completing partial point clouds and then learning action-conditioned dynamics in this completed 3D scene. By operating on completed geometry, 3DPWM enables reliable long-horizon rollouts and more accurate cost evaluation for model-based planning while supporting adaptation to new tasks. Experiments across different robotic embodiments and tabletop manipulation benchmarks demonstrate that 3DPWM achieves significantly more reliable long-horizon rollouts (100-300+ steps), supports both open-loop and closed-loop planning, and enables successful sim-to-real transfer.
