---
title: "薛定谔的猫：场景运动学的概率表示与预测"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.25984v1"
permalink: "https://feed.astralor.com/items/itm_09cad5bf938569cb009b078e"
published: "2026-07-28T17:05:39.000Z"
score: 75
channel: "ai"
tags: ["GARFIELD","不确定性感知规划","场景运动学","概率建模"]
---

# 薛定谔的猫：场景运动学的概率表示与预测

## AI 点评

> 这篇论文很值得关注，因为它将概率建模的灵活性推到了新的高度——不仅预测未来可能性，还能量化每个元素的不确定性并实时交互。对从事机器人操控或自动驾驶规划的研究者尤其重要，GARFIELD的采样速度意味着可以嵌入到闭环系统中做实时决策。不过要警惕目前只在受限数据集上测试，距离真实世界的通行性还有距离。

## 速览

论文提出GARFIELD模型，通过结构化时空潜在分布对场景运动学进行概率建模。该模型可同时采样所有轨迹并通过确定性密度解码器直接获取运动分布，将不确定性定位到具体元素和时间步。相比大型视频生成模型，采样轨迹快97倍，密度估计快两个数量级，适用于交互式探索和不确定性感知规划。

## 关键点

- GARFIELD利用结构化潜在分布对场景运动学的概率进行建模，支持多轨迹联合采样。
- 确定性密度解码器可直接估计运动密度，无需蒙特卡洛采样，速度提升两个数量级。
- 不确定性可定位到特定场景元素和时间步，并可通过附加约束逐步细化。
- 在运动规划任务上达到与大型视频生成模型相当的性能，但采样速度提升97倍。

## 全文

Predicting how a scene may evolve from partial observations requires reasoning about multiple possible futures rather than committing to a single trajectory. Existing approaches either generate appearance-dominated video predictions or sample a small number of trajectories without explicitly modeling the distribution of possible motion. We introduce Goal-Aware Representations of Future kInEmatic Latent Distributions (GARFIELD), a probabilistic model of scene kinematics that learns a structured spatio-temporal latent representation of the distribution over possible futures given an image and optional spatio-temporally sparse constraints. The same latent representation enables both joint sampling of all trajectories and direct access to the underlying motion distribution through an efficient deterministic density decoder. As a result, uncertainty about future motion can be localized to specific scene elements and timesteps and progressively refined through additional constraints. Experiments demonstrate strong motion planning performance competitive with large video generation models while sampling trajectories $97\times$ faster. Our method further estimates motion densities two orders of magnitude faster than Monte-Carlo sampling from motion generation models, enabling interactive exploration and uncertainty-aware planning.

---

引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_09cad5bf938569cb009b078e