Abstract: Systems, methods, and devices are disclosed herein for generating synthetic data for training computer vision models using an object-composable NeRF model that reduces the sim-to-real gap for perception-based tasks. In one example, a method includes generating a synthetic dataset using the NeRF model, wherein dataset includes both photorealistic renderings and multiple types of 2D and 3D supervision, including depth maps, segmentation masks, and meshes. To generate the dataset, the NeRF model receives as input a real image of a real scene having objects and a background, extracts a feature volume for each object, and renders one or more synthetic scenes using the sampled objects. The method further includes training a perception model based at least in part on the synthetic dataset and controlling a robotic system based at least in part on output from the trained perception model.

Abstract: Various embodiments of the technology described herein generally relate to systems and methods for trajectory optimization with machine learning techniques. More specifically, certain embodiments relate to using neural networks to quickly predict optimized robotic arm trajectories in a variety of scenarios. Systems and methods described herein use deep neural networks to quickly predict optimized robotic arm trajectories according to certain constraints. Optimization, in accordance with some embodiments of the present technology, may include optimizing trajectory geometry and dynamics while satisfying a number of constraints, including staying collision-free, and minimizing the time it takes to complete the task.

Abstract: Various embodiments of the technology described herein generally relate to systems and methods for trajectory optimization with machine learning techniques. More specifically, certain embodiments relate to using neural networks to quickly predict optimized robotic arm trajectories in a variety of scenarios. Systems and methods described herein use deep neural networks to quickly predict optimized robotic arm trajectories according to certain constraints. Optimization, in accordance with some embodiments of the present technology, may include optimizing trajectory geometry and dynamics while satisfying a number of constraints, including staying collision-free and minimizing the time it takes to complete the task.

Abstract: Various embodiments of the technology described herein generally relate to systems and methods for trajectory optimization with machine learning techniques. More specifically, certain embodiments relate to using neural networks to quickly predict optimized robotic arm trajectories in a variety of scenarios. Systems and methods described herein use deep neural networks to quickly predict optimized robotic arm trajectories according to certain constraints. Optimization, in accordance with some embodiments of the present technology, may include optimizing trajectory geometry and dynamics while satisfying a number of constraints, including staying collision-free and minimizing the time it takes to complete the task.

Abstract: Various embodiments of the technology described herein generally relate to systems and methods for trajectory optimization with machine learning techniques. More specifically, certain embodiments relate to using neural networks to quickly predict optimized robotic arm trajectories in a variety of scenarios. Systems and methods described herein use deep neural networks to quickly predict optimized robotic arm trajectories according to certain constraints. Optimization, in accordance with some embodiments of the present technology, may include optimizing trajectory geometry and dynamics while satisfying a number of constraints, including staying collision-free, and minimizing the time it takes to complete the task.

Abstract: Various embodiments of the technology described herein generally relate to systems and methods for trajectory optimization with machine learning techniques. More specifically, certain embodiments relate to using neural networks to quickly predict optimized robotic arm trajectories in a variety of scenarios. Systems and methods described herein use deep neural networks to quickly predict optimized robotic arm trajectories according to certain constraints. Optimization, in accordance with some embodiments of the present technology, may include optimizing trajectory geometry and dynamics while satisfying a number of constraints, including staying collision-free and minimizing the time it takes to complete the task.

Abstract: Various embodiments of the present technology generally relate to robotic devices, computer vision, and artificial intelligence. More specifically, some embodiments relate to object tracking using neural networks and computer vision systems. In some embodiments, a computer vision system for object tracking captures one or more images of a first scene, wherein the first scene corresponds to a first location, identifies a distinct object in the first scene based on the one or more first images, directs a robotic device to move the distinct object from the first location to a second location, captures one or more second images of a second scene, wherein the second scene corresponds to the second location, and determines if the distinct objects is in the second scene based on the one or more second images.