Dense packing in pick-and-place systems is an important feature in many warehouse and logistics applications. Prior work in this space has largely focused on planning algorithms in simulation, but real-world packing performance is often bottlenecked by the difficulty of perceiving 3D object geometry in highly occluded, partially observed scenes. In this work, we present a fully-convolutional shape completion model, F-CON, which can be easily combined with off-the-shelf planning methods for dense packing in the real world. We also release a simulated dataset, COB-3D-v2, that can be used to train shape completion models for real-word robotics applications, and use it to demonstrate that F-CON outperforms other state-of-the-art shape completion methods. Finally, we equip a real-world pick-and-place system with F-CON, and demonstrate dense packing of complex, unseen objects in cluttered scenes. Across multiple planning methods, F-CON enables substantially better dense packing than other shape completion methods.

The infrastructural layer is TensorFlow for differentiable(derivatives) programming. It's a framework at its core to manipulate N-Dimensional arrays(i.e. These are much like NumPy arrays. Although some differences can be noticed. There are such difficulties to accelerate GPUs and TPUs by NumPy, but TensorFlow can leverage the hardware accelerators.

In this article we are going to measure text similarity using BERT. For the task we will be using pytorch a deep learning library in python.

A central tenet of probabilistic programming is that a model is specified exactly once in a canonical representation which is usable by inference algorithms. We describe JointDistributions, a family of declarative representations of directed graphical models in TensorFlow Probability.

This problem appeared as an assignment in the online coursera course Convolution Neural Networks by Prof Andrew Ng, (deeplearing.ai). The description of the problem is taken straightway from the assignment. Most of the algorithms we've studied optimize a cost function to get a set of parameter values. In Neural Style Transfer, we shall optimize a cost function to get pixel values! Neural Style Transfer (NST) is one of the most fun techniques in deep learning.