Abstract: Embodiments of the present disclosure are directed to methods, computer program products, and computer systems of a robotic apparatus with robotic instructions replicating a food preparation recipe.

Abstract: Embodiments of the present disclosure are directed to methods, computer program products, and computer systems of a robotic apparatus with robotic instructions replicating a food preparation recipe.

Abstract: Embodiments of the present disclosure are directed to methods, computer program products, and computer systems of a robotic apparatus with robotic instructions replicating a food preparation recipe.

Abstract: Embodiments of the present disclosure are directed to methods, computer program products, and computer systems of a robotic apparatus with robotic instructions replicating a food preparation recipe.

Abstract: Systems and methods are provided for managing a robotic assistant. Environment data corresponding to a current environment is collected to determine a type of the current environment based on the collected environment data. One or more objects in the current environment are detected. The one or more objects are associated with the type of the current environment. For each of the one or more objects, one or more interactions are identified based on a type of the respective object and the type of the current environment. Object libraries corresponding to the one or more objects are downloaded. The object libraries include interaction data corresponding to the respective identified one or more interactions. At least a portion of the one or more interactions are executed upon the respective one or more objects.

Abstract: The present disclosure is directed to methods, computer program products, and computer systems of a robotic kitchen hub for calibrations of multi-functional robotic platforms for commercial and residential environments with artificial intelligence and machine learning. The multi-functional robotic platform includes a robotic kitchen for calibration with either a joint state trajectory or in a coordinate system like a cartesian coordinate for mass installation of robotic kitchens. Calibration verifications and minimanipulation library adaptation and adjustment of any serial model or different models provide scalability in the mass manufacturing of a robotic kitchen system. A robotic kitchen with multi-mode provides a robot mode, a collaboration mode and a user mode which a particular food dish can be prepared by the robot, a collaboration on sharing tasks between the robot and a user, or the robot serves as an aid for the user to prepare a food dish.

Abstract: The present disclosure is directed to methods, computer program products, and computer systems of a robotic kitchen hub for minimanipulation library adjustments and calibrations of multi-functional robotic platforms for commercial and residential environments with artificial intelligence and machine learning. The multi-functional robotic platform includes a robotic kitchen for calibration with either a joint state trajectory or in a coordinate system like a cartesian coordinate for mass installation of robotic kitchens. Calibration verifications and minimanipulation library adaptation and adjustment of any serial model or different models provide scalability in the mass manufacturing of a robotic kitchen system. A robotic kitchen with multi-mode provides a robot mode, a collaboration mode and a user mode which a particular food dish can be prepared by the robot, a collaboration on sharing tasks between the robot and a user, or the robot serves as an aid for the user to prepare a food dish.

Abstract: The present disclosure is directed to methods, computer program products, and computer systems for instructing a robot to prepare a food dish by replacing the human chef's movements and actions. Monitoring a human chef is carried out in an instrumented application-specific setting, a standardized robotic kitchen in this instance, and involves using sensors and computers to watch, monitor, record and interpret the motions and actions of the human chef, in order to develop a robot-executable set of commands robust to variations and changes in the environment, capable of allowing a robotic or automated system in a robotic kitchen to prepare the same dish to the standards and quality as the dish prepared by the human chef.

Abstract: The present disclosure is directed to methods, computer program products, and computer systems of a multi-functional robotic platform including a robotic kitchen for calibration with either a joint state trajectory or in a coordinate system like a cartesian coordinate for mass installation of robotic kitchens, multi-mode operations of the robotic kitchen to provide different ways to prepare food dishes, and subsystems tailored to operate and interact with the various elements of a robotic kitchen, such as the robotic effectors, other subsystems, and containers, ingredients. Calibration verifications and minimanipulation library adaptation and adjustment of any serial model or different models provide scalability in the mass manufacturing of a robotic kitchen system, as well as methods as to how each manufactured robotic kitchen system meets the operational requirements.

Abstract: The present disclosure is directed to a method and a system which trains object detection neural networks with a dependency based loss function for capturing dependent training images. The object detection neural networks system comprises a calibrated camera system for capturing images for the object detection neural network model and the dependent based loss function to process dependent training images, which is then fed to an optimizer to adjust parameters of the object detection neural network model to minimize the loss value. Additional penalties can be imposed by knowledge base rules. A camera system in the object detection neural networks system can include cameras with a fixed distance between neighboring cameras, or unaligned cameras arranged at various distances and/or angles between them, with an option to add sensors to the camera system.

Abstract: Embodiments of the present disclosure are directed to methods, computer program products, and computer systems of a robotic apparatus with robotic instructions replicating a food preparation recipe.

Abstract: Embodiments of the present disclosure are directed to methods, computer program products, and computer systems of a robotic apparatus with robotic instructions replicating a food preparation recipe.

Abstract: The present disclosure is directed to methods, computer program products, and computer systems for instructing a robot to prepare a food dish by replacing the human chef's movements and actions. Monitoring a human chef is carried out in an instrumented application-specific setting, a standardized robotic kitchen in this instance, and involves using sensors and computers to watch, monitor, record and interpret the motions and actions of the human chef, in order to develop a robot-executable set of commands robust to variations and changes in the environment, capable of allowing a robotic or automated system in a robotic kitchen to prepare the same dish to the standards and quality as the dish prepared by the human chef.

Abstract: Systems and methods are provided for managing a robotic assistant. Environment data corresponding to a current environment is collected to determine a type of the current environment based on the collected environment data. One or more objects in the current environment are detected. The one or more objects are associated with the type of the current environment. For each of the one or more objects, one or more interactions are identified based on a type of the respective object and the type of the current environment. Object libraries corresponding to the one or more objects are downloaded. The object libraries include interaction data corresponding to the respective identified one or more interactions. At least a portion of the one or more interactions are executed upon the respective one or more objects.

Abstract: The present disclosure is directed to methods, computer program products, and computer systems for instructing a robot to prepare a food dish by replacing the human chef's movements and actions. Monitoring a human chef is carried out in an instrumented application-specific setting, a standardized robotic kitchen in this instance, and involves using sensors and computers to watch, monitor, record and interpret the motions and actions of the human chef, in order to develop a robot-executable set of commands robust to variations and changes in the environment, capable of allowing a robotic or automated system in a robotic kitchen to prepare the same dish to the standards and quality as the dish prepared by the human chef.

Abstract: The present disclosure is directed to methods, computer program products, and computer systems for instructing a robot to prepare a food dish by replacing the human chef's movements and actions. Monitoring a human chef is carried out in an instrumented application-specific setting, a standardized robotic kitchen in this instance, and involves using sensors and computers to watch, monitor, record and interpret the motions and actions of the human chef, in order to develop a robot-executable set of commands robust to variations and changes in the environment, capable of allowing a robotic or automated system in a robotic kitchen to prepare the same dish to the standards and quality as the dish prepared by the human chef.

Abstract: The present disclosure is directed to methods, computer program products, and computer systems for instructing a robot to prepare a food dish by replacing the human chef's movements and actions. Monitoring a human chef is carried out in an instrumented application-specific setting, a standardized robotic kitchen in this instance, and involves using sensors and computers to watch, monitor, record and interpret the motions and actions of the human chef, in order to develop a robot-executable set of commands robust to variations and changes in the environment, capable of allowing a robotic or automated system in a robotic kitchen to prepare the same dish to the standards and quality as the dish prepared by the human chef.

Abstract: The present disclosure is directed to methods, computer program products, and computer systems for instructing a robot to prepare a food dish by replacing the human chef's movements and actions. Monitoring a human chef is carried out in an instrumented application-specific setting, a standardized robotic kitchen in this instance, and involves using sensors and computers to watch, monitor, record and interpret the motions and actions of the human chef, in order to develop a robot-executable set of commands robust to variations and changes in the environment, capable of allowing a robotic or automated system in a robotic kitchen to prepare the same dish to the standards and quality as the dish prepared by the human chef.

Abstract: This disclosure discloses various technical features for creating robotic humanoid movements, actions, and interactions with tools and the instrumented environment by automatically building movements for the humanoid; actions and behaviors of the humanoid based on a set of computer-encoded robotic movement and action primitives. The primitives are defined by motions/actions of articulated degrees of freedom that range in complexity from simple to complex, and which can be combined in any form in serial/parallel fashion. These motion-primitives are termed to be minimanipulations and each has a clear time-indexed command input-structure and output behavior/performance profile that is intended to achieve a certain function. Minimanipulations comprise a new way of creating a programmable-by-example platform for robots.