Neural Radiance Fields (NeRFs) are coordinate-based implicit representations of 3D scenes that use a differentiable rendering procedure to learn a representation of an environment from images. This paper extends NeRFs to handle dynamic scenes in an online fashion. We do so by introducing a particle-based parametric encoding, which allows the intermediate NeRF features -- now coupled to particles in space -- to be moved with the dynamic geometry. We backpropagate the NeRF's photometric reconstruction loss into the position of the particles in addition to the features they are associated with. The position gradients are interpreted as particle velocities and integrated into positions using a position-based dynamics (PBS) physics system. Introducing PBS into the NeRF formulation allows us to add collision constraints to the particle motion and creates future opportunities to add other movement priors into the system such as rigid and deformable body constraints. We show that by allowing the features to move in space, we incrementally adapt the NeRF to the changing scene.

Fabric manipulation is a long-standing challenge in robotics due to the enormous state space and complex dynamics. Learning approaches stand out as promising for this domain as they allow us to learn behaviours directly from data. Most prior methods however rely heavily on simulation, which is still limited by the large sim-to-real gap of deformable objects or rely on large datasets. A promising alternative is to learn fabric manipulation directly from watching humans perform the task. In this work, we explore how demonstrations for fabric manipulation tasks can be collected directly by human hands, providing an extremely natural and fast data collection pipeline. Then, using only a handful of such demonstrations, we show how a sample-efficient pick-and-place policy can be learned and deployed on a real robot, without any robot data collection at all. We demonstrate our approach on a fabric folding task, showing that our policy can reliably reach folded states from crumpled initial configurations.

我们表明，如果考虑密度感知的认知不确定性项，则有效地量化神经辐射场（NERF）中的模型不确定性。在先前的工作中调查的幼稚合奏简单地渲染了RGB图像，以量化因观察到的场景的解释而引起的模型不确定性。相比之下，我们还考虑了各个射线沿线的终止概率，以确定认知模型的不确定性，因为对训练过程中未观察到的场景部分的知识不足。我们在NERF的既定不确定性量化基准中实现了新的最先进的性能，优于需要对NERF架构和培训制度进行复杂更改的方法。我们此外表明，可以将NERF不确定性用于次要视图选择和模型改进。

在社会背景下的算法决策，例如零售定价，贷款管理，在线平台上的建议等，通常涉及为了学习而进行决策的实验，这导致受这些决策影响的人们的不公平感知。因此，有必要在此类决策过程中嵌入适当的公平概念。本文的目的是通过一种新颖的元观念来强调公平的时间概念与在线决策之间的丰富界面，以确保在决策时确保公平。考虑到静态决策的一些任意比较公平概念（例如，学生最多应支付一般成人价格的90％），如果满足上述公平概念，则相应的在线决策算法在决策时满足公平性对于任何与过去的决定相比，收到决定的任何实体。我们表明，这一基本要求引入了在线决策中的新方法论挑战。我们说明了在随机凸优化的背景下，在比较公平的约束下，在随机凸优化的背景下解决这些挑战所必需的新方法，该方法取决于实体所收到的决策，这取决于过去每个人都收到的决策。该论文展示了由于时间公平的关注而引起的在线决策中的新研究机会。