Variational autoencoder (VAE) is a popular method for drug discovery and there had been a great deal of architectures and pipelines proposed to improve its performance. But the VAE model itself suffers from deficiencies such as poor manifold recovery when data lie on low-dimensional manifold embedded in higher dimensional ambient space and they manifest themselves in each applications differently. The consequences of it in drug discovery is somewhat under-explored. In this paper, we study how to improve the similarity of the data generated via VAE and the training dataset by improving manifold recovery via a 2-stage VAE where the second stage VAE is trained on the latent space of the first one. We experimentally evaluated our approach using the ChEMBL dataset as well as a polymer datasets. In both dataset, the 2-stage VAE method is able to improve the property statistics significantly from a pre-existing method.

We study the problem of designing models for machine learning tasks defined on sets. In contrast to traditional approach of operating on fixed dimensional vectors, we consider objective functions defined on sets that are invariant to permutations. Such problems are widespread, ranging from estimation of population statistics [1], to anomaly detection in piezometer data of embankment dams [2], to cosmology [3,4]. Our main theorem characterizes the permutation invariant functions and provides a family of functions to which any permutation invariant objective function must belong. This family of functions has a special structure which enables us to design a deep network architecture that can operate on sets and which can be deployed on a variety of scenarios including both unsupervised and supervised learning tasks. We also derive the necessary and sufficient conditions for permutation equivariance in deep models. We demonstrate the applicability of our method on population statistic estimation, point cloud classification, set expansion, and outlier detection.

The precise control of soft and continuum robots requires knowledge of their shape. The shape of these robots has, in contrast to classical rigid robots, infinite degrees of freedom. To partially reconstruct the shape, proprioceptive techniques use built-in sensors resulting in inaccurate results and increased fabrication complexity. Exteroceptive methods so far rely on placing reflective markers on all tracked components and triangulating their position using multiple motion-tracking cameras. Tracking systems are expensive and infeasible for deformable robots interacting with the environment due to marker occlusion and damage. Here, we present a regression approach for 3D shape estimation using a convolutional neural network. The proposed approach takes advantage of data-driven supervised learning and is capable of real-time marker-less shape estimation during inference. Two images of a robotic system are taken simultaneously at 25 Hz from two different perspectives, and are fed to the network, which returns for each pair the parameterized shape. The proposed approach outperforms marker-less state-of-the-art methods by a maximum of 4.4\% in estimation accuracy while at the same time being more robust and requiring no prior knowledge of the shape. The approach can be easily implemented due to only requiring two color cameras without depth and not needing an explicit calibration of the extrinsic parameters. Evaluations on two types of soft robotic arms and a soft robotic fish demonstrate our method's accuracy and versatility on highly deformable systems in real-time. The robust performance of the approach against different scene modifications (camera alignment and brightness) suggests its generalizability to a wider range of experimental setups, which will benefit downstream tasks such as robotic grasping and manipulation.

为了使软机器人在以人为本的环境中有效工作，他们需要能够根据（本体感受）传感器估算其状态和外部相互作用。估计干扰使软机器人可以执行理想的力控制。即使在刚性操纵器的情况下，最终效应器的力估计也被视为一个非平凡的问题。实际上，其他当前应对这一挑战的方法也存在防止其一般应用的缺点。它们通常基于简化的软动力学模型，例如依赖于零件的恒定曲率（PCC）近似值或匹配的刚体模型的模型，这些模型并不代表该问题的细节。因此，无法构建复杂的人类机器人互动所需的应用。有限元方法（FEM）允许以更通用的方式预测软机器人动力学。在这里，使用框架沙发的软机器人建模功能，我们构建了一个详细的FEM模型，该模型由多段的软连续机器人手臂组成，该机器人由合规的可变形材料和纤维增强的压力驱动室组成，并具有用于提供方向输出的传感器的模型。该模型用于为操纵器建立状态观察者。校准模型参数以使用物理实验匹配手动制造过程的缺陷。然后，我们解决了二次编程逆动力学问题，以计算解释姿势误差的外力的组成部分。我们的实验显示，平均力估计误差约为1.2％。由于提出的方法是通用的，因此这些结果令人鼓舞，该任务是构建可以在以人为中心的环境中部署的复杂，反应性，基于传感器的行为的软机器人。