Other

Embodied Intelligence Inc.

Mohammadreza Rohaninejad

Rohaninejad, Mohammadreza

Mostafa Rohaninejad

Morteza Rohaninejad

Seyed Ali Mirnajafi

Andrew Amir Vaziri

Xi Chen

Yan Duan

Nikhil Mishra

Yu Xuan Liu

Ian Rust

Yide Shentu

Haoran Tang

Carlos Florensa

Various embodiments of the present technology generally relate to robotic devices and artificial intelligence. More specifically, some embodiments relate to a robotic device for picking items from a bin and perturbing items in a bin. In some implementations, the device may include one or more computer-vision systems. A computer-vision system, in accordance with the present technology, may use at least two two-dimensional images to generate three-dimensional (3D) information about the bin and items in the bin. Based on the 3D information, a strategy for picking up items from the bin is determined. When no strategies with high probability of success exist, the robotic device may perturb the contents of the bin to create new available pick-up points and re-attempt to pick up an item.

Systems and methods for robotic picking

Various embodiments of the present technology generally relate to robotic devices, computer-vision systems, and artificial intelligence. More specifically, some embodiments relate to a computer-vision system for robotic devices. In some implementations, a computer-vision is coupled to a robotic arm for picking items from a bin and perturbing items in a bin. A computer-vision system, in accordance with the present technology, may use at least two two-dimensional (2D) images to generate three-dimensional (3D) information about the bin and items in the bin. A method of training artificial neural networks to generate 3D information from 2D images includes obtaining ground truth data for a scene, obtaining at least two images of the scene, and providing the ground truth data and the at least two images to the artificial neural network configured to generate a depth map from the images based on the ground truth data.

Determine depth with pixel-to-pixel image correspondence for three-dimensional computer vision

Various embodiments of the technology described herein generally relate to robotic systems for interacting with objects in a warehouse environment. More specifically, certain embodiments relate to systems and methods for collecting data related to robotic picking of objects through test interactions. In some embodiments, a robotic device may work in collaboration with a computer vision system for collecting data related to new objects in a warehouse, commercial, industrial, or similar environment. A robotic picking system may operate in a data collection mode during which objects are sent to a robotic picking device for data collection during one or more test interactions or test stimuli. The test interactions and stimuli may be used to produce a whitelist of objects that the robotic picking device may attempt to pick up during regular operation.

Methods of collecting data through test interactions

A system for magnetically suspending tissue includes a grasper placement tool, a tissue grasper, a magnetic coupling element, and a tether which secures the magnetic coupling element to the tissue grasper. The grasper placement tool is used to simultaneously introduce both the tissue grasper and the magnetic coupling element to a body cavity. The grasper then releases the magnetic coupling element and engages a target tissue structure. The tissue grasper is then detached from the placement tool and a conventional laparoscopic or other grasper is used to engage the magnetic coupling element to an external magnet.

Methods and systems for magnetically suspending tissue structures

Growing community of inventors

Saratoga, CA, United States of America

Mostafa Rohaninejad