Recently, AutoFlow has shown promising results on learning a training set for optical flow, but requires ground truth labels in the target domain to compute its search metric. Observing a strong correlation between the ground truth search metric and self-supervised losses, we introduce self-supervised AutoFlow to handle real-world videos without ground truth labels. Using self-supervised loss as the search metric, our self-supervised AutoFlow performs on par with AutoFlow on Sintel and KITTI where ground truth is available, and performs better on the real-world DAVIS dataset. We further explore using self-supervised AutoFlow in the (semi-)supervised setting and obtain competitive results against the state of the art.
translated by 谷歌翻译
大型语言模型(LLM)从人类的指示中解开了任务计划的新功能。但是,事先尝试将LLMS应用于现实世界的机器人任务受到周围场景中缺乏接地的限制。在本文中,我们开发了NLMAP,这是一个开放式摄影和可查询场景表示,以解决此问题。 NLMAP是一个框架,可以将上下文信息收集到LLM计划者中,从而在生成上下文条件条件计划之前,可以在场景中查看和查询可用的对象。 NLMAP首先使用视觉语言模型(VLM)建立自然语言可查询场景表示。基于LLM的对象建议模块解析指令并提出涉及的对象,以查询场景表示以获取对象可用性和位置。然后,LLM规划师计划提供有关场景的此类信息。 NLMAP允许机器人在没有固定的对象列表或可执行选项的情况下操作,从而使真实的机器人操作无法通过以前的方法实现。项目网站:https://nlmap-saycan.github.io
translated by 谷歌翻译
将简单的体系结构与大规模预训练相结合已导致图像分类的大量改进。对于对象检测,预训练和缩放方法的确定性不佳,尤其是在长尾和开放式摄影的环境中,训练数据相对较少。在本文中,我们提出了一个强大的配方,用于将图像文本模型转移到开放式对象检测中。我们使用具有最小修改,对比度文本预训练和端到端检测微调的标准视觉变压器体系结构。我们对该设置的缩放属性的分析表明,增加图像级预训练和模型大小在下游检测任务上产生一致的改进。我们提供适应性策略和正规化,以实现零击文本条件和单次图像条件对象检测的非常强劲的性能。代码和型号可在GitHub上找到。
translated by 谷歌翻译
以对象为中心的表示是通过提供柔性抽象可以在可以建立的灵活性抽象来实现更系统的推广的有希望的途径。最近的简单2D和3D数据集的工作表明,具有对象的归纳偏差的模型可以学习段,并代表单独的数据的统计结构中的有意义对象,而无需任何监督。然而,尽管使用越来越复杂的感应偏差(例如,用于场景的尺寸或3D几何形状),但这种完全无监督的方法仍然无法扩展到不同的现实数据。在本文中,我们采取了弱监督的方法,并专注于如何使用光流的形式的视频数据的时间动态,2)调节在简单的对象位置上的模型可以用于启用分段和跟踪对象在明显更现实的合成数据中。我们介绍了一个顺序扩展,以便引入我们训练的推出,我们训练用于预测现实看的合成场景的光流,并显示调节该模型的初始状态在一小组提示,例如第一帧中的物体的质量中心,是足以显着改善实例分割。这些福利超出了新型对象,新颖背景和更长的视频序列的培训分配。我们还发现,在推论期间可以使用这种初始状态调节作为对特定物体或物体部分的型号查询模型,这可能会为一系列弱监管方法铺平,并允许更有效的互动训练有素的型号。
translated by 谷歌翻译
Given a dataset of expert agent interactions with an environment of interest, a viable method to extract an effective agent policy is to estimate the maximum likelihood policy indicated by this data. This approach is commonly referred to as behavioral cloning (BC). In this work, we describe a key disadvantage of BC that arises due to the maximum likelihood objective function; namely that BC is mean-seeking with respect to the state-conditional expert action distribution when the learner's policy is represented with a Gaussian. To address this issue, we introduce a modified version of BC, Adversarial Behavioral Cloning (ABC), that exhibits mode-seeking behavior by incorporating elements of GAN (generative adversarial network) training. We evaluate ABC on toy domains and a domain based on Hopper from the DeepMind Control suite, and show that it outperforms standard BC by being mode-seeking in nature.
translated by 谷歌翻译
This contribution presents a deep learning method for the extraction and fusion of information relating to kidney stone fragments acquired from different viewpoints of the endoscope. Surface and section fragment images are jointly used during the training of the classifier to improve the discrimination power of the features by adding attention layers at the end of each convolutional block. This approach is specifically designed to mimic the morpho-constitutional analysis performed in ex-vivo by biologists to visually identify kidney stones by inspecting both views. The addition of attention mechanisms to the backbone improved the results of single view extraction backbones by 4% on average. Moreover, in comparison to the state-of-the-art, the fusion of the deep features improved the overall results up to 11% in terms of kidney stone classification accuracy.
translated by 谷歌翻译
The process of screening molecules for desirable properties is a key step in several applications, ranging from drug discovery to material design. During the process of drug discovery specifically, protein-ligand docking, or chemical docking, is a standard in-silico scoring technique that estimates the binding affinity of molecules with a specific protein target. Recently, however, as the number of virtual molecules available to test has rapidly grown, these classical docking algorithms have created a significant computational bottleneck. We address this problem by introducing Deep Surrogate Docking (DSD), a framework that applies deep learning-based surrogate modeling to accelerate the docking process substantially. DSD can be interpreted as a formalism of several earlier surrogate prefiltering techniques, adding novel metrics and practical training practices. Specifically, we show that graph neural networks (GNNs) can serve as fast and accurate estimators of classical docking algorithms. Additionally, we introduce FiLMv2, a novel GNN architecture which we show outperforms existing state-of-the-art GNN architectures, attaining more accurate and stable performance by allowing the model to filter out irrelevant information from data more efficiently. Through extensive experimentation and analysis, we show that the DSD workflow combined with the FiLMv2 architecture provides a 9.496x speedup in molecule screening with a <3% recall error rate on an example docking task. Our open-source code is available at https://github.com/ryienh/graph-dock.
translated by 谷歌翻译
Molecular shape and geometry dictate key biophysical recognition processes, yet many graph neural networks disregard 3D information for molecular property prediction. Here, we propose a new contrastive-learning procedure for graph neural networks, Molecular Contrastive Learning from Shape Similarity (MolCLaSS), that implicitly learns a three-dimensional representation. Rather than directly encoding or targeting three-dimensional poses, MolCLaSS matches a similarity objective based on Gaussian overlays to learn a meaningful representation of molecular shape. We demonstrate how this framework naturally captures key aspects of three-dimensionality that two-dimensional representations cannot and provides an inductive framework for scaffold hopping.
translated by 谷歌翻译
为了成为人类的有效伴侣,机器人必须越来越舒适地与环境接触。不幸的是,机器人很难区分``足够的''和``太多''力:完成任务需要一些力量,但太多可能会损害设备或伤害人类。设计合规的反馈控制器(例如刚度控制)的传统方法需要对控制参数进行手工调整,并使建立安全,有效的机器人合作者变得困难。在本文中,我们提出了一种新颖而易于实现的力反馈控制器,该反馈控制器使用控制屏障功能(CBF)直接从用户的最大允许力和扭矩的用户规格中得出合并的控制器。我们比较了传统僵硬控制的方法,以证明控制架构的潜在优势,并在人类机器人协作任务中证明了控制器的有效性:对笨重对象的合作操纵。
translated by 谷歌翻译
我们开发了NL2接口,以探索从自然语言查询中生成可用的交互式多访问界面的潜力。借助NL2系列,用户可以直接编写自然语言查询,以自动生成完全交互式的多访问界面,而无需任何额外的学习工具或编程语言。此外,用户可以与接口进行交互,以轻松地转换数据并快速看到可视化中的结果。
translated by 谷歌翻译