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.