论文标题
通过空间世界模型跟踪和计划
Tracking and Planning with Spatial World Models
论文作者
论文摘要
我们介绍了一种实时导航和跟踪方法，并通过不同的世界模型进行跟踪。控制模型为机器人和计算机游戏带来了令人印象深刻的结果，但是这种成功尚未扩展到基于视觉的导航。为了解决这个问题，我们将可区分渲染的新兴领域的进步转移到基于模型的控制中。我们通过在学习的3D空间世界模型中进行计划，并结合先前在TSDF Fusion背景下使用的姿势估计算法，但现在针对我们的设置量身定制并改进了以结合代理动力学。我们根据复杂的人体设计的平面图评估了六个模拟环境，并提供定量结果。我们仅在随机，连续动力学下仅使用图像和深度观测来以15 Hz的频率实现高达92％的导航成功率。
We introduce a method for real-time navigation and tracking with differentiably rendered world models. Learning models for control has led to impressive results in robotics and computer games, but this success has yet to be extended to vision-based navigation. To address this, we transfer advances in the emergent field of differentiable rendering to model-based control. We do this by planning in a learned 3D spatial world model, combined with a pose estimation algorithm previously used in the context of TSDF fusion, but now tailored to our setting and improved to incorporate agent dynamics. We evaluate over six simulated environments based on complex human-designed floor plans and provide quantitative results. We achieve up to 92% navigation success rate at a frequency of 15 Hz using only image and depth observations under stochastic, continuous dynamics.
