Patents Assigned to Embodied Intelligence Inc.
  • Patent number: 11951636
    Abstract: 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.
    Type: Grant
    Filed: January 28, 2021
    Date of Patent: April 9, 2024
    Assignee: Embodied Intelligence Inc.
    Inventors: Andrew Vaziri, Mostafa Rohaninejad
  • Patent number: 11911901
    Abstract: Various embodiments of the present technology generally relate to robotic devices and artificial intelligence. More specifically, some embodiments relate to an artificial neural network training method that does not require extensive training data or time expenditure. The few-shot training model disclosed herein includes attempting to pick up items and, in response to a failed pick up attempt, transferring and generalizing information to similar regions to improve probability of success in future attempts. In some implementations, the training method is used to robotic device for picking items from a bin and perturbing items in a bin. When no picking strategies with high probability of success exist, the robotic device may perturb the contents of the bin to create new available pick-up points. In some implementations, the device may include one or more Computer-vision systems.
    Type: Grant
    Filed: September 8, 2020
    Date of Patent: February 27, 2024
    Assignee: Embodied Intelligence, Inc.
    Inventors: Yan Duan, Haoran Tang, Yide Shentu, Nikhil Mishra, Xi Chen
  • Patent number: 11911903
    Abstract: 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. The robotic device may include one or more picking elements and one or more perturbation elements for disturbing a present arrangement of items in the bin. In an exemplary embodiment, a perturbation element comprises a compressed air valve. In some implementations, the robotic device may also include one or more computer-vision systems. Based on image data from the one or more computer-vision systems, 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.
    Type: Grant
    Filed: September 8, 2020
    Date of Patent: February 27, 2024
    Assignee: Embodied Intelligence, Inc.
    Inventors: Yan Duan, Ian Rust, Andrew Amir Vaziri, Xi Chen, Carlos Florensa
  • Publication number: 20210276185
    Abstract: Various embodiments of the present technology generally relate to robotic devices, artificial intelligence, and computer vision. More specifically, some embodiments relate to an imaging process for detecting failure modes in a robotic motion environment. In one embodiment, a method of detecting failure modes in a robotic motion environment comprises collecting one or more images of a multiple scenes throughout a robotic motion cycle. Images may be collected by one or more cameras positioned at one or more locations for collecting images with various views. Images collected throughout the robotic motion cycle may be processed in real-time to determine if any failure modes are present in their respective scenes, report when failure modes are present, and may be used to direct a robotic device accordingly.
    Type: Application
    Filed: March 5, 2021
    Publication date: September 9, 2021
    Applicant: Embodied Intelligence Inc.
    Inventors: Yide Shentu, David Mascharka, Tianhao Zhang, Yan Duan, Jasmine Deng, Xi Chen
  • Publication number: 20210276187
    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.
    Type: Application
    Filed: March 5, 2021
    Publication date: September 9, 2021
    Applicant: Embodied Intelligence Inc.
    Inventors: Haoran Tang, Xi Chen, Yan Duan, Nikhil Mishra, Shiyao Wu, Maximilian Sieb, Yide Shentu
  • Publication number: 20210276188
    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.
    Type: Application
    Filed: March 5, 2021
    Publication date: September 9, 2021
    Applicant: Embodied Intelligence Inc.
    Inventors: Haoran Tang, Xi Chen, Yan Duan, Nikhil Mishra, Shiyao Wu, Maximilian Sieb, Yide Shentu