The present invention relates to a method, such as a surgical method for assisting a surgeon for placing screws in the spine using a robot attached to a passive structure. The present invention also related to a method, such as a surgical method for assisting a surgeon for removing volumes in the body of a patient using a robot attached to a passive structure and to a device to carry out said methods. The present invention further concerns a device suitable to carry out the methods according to the present invention.

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. In one embodiment, a robotic control platform, comprises one or more sensors; a mechanical robotic structure including one or more end effectors, and one or more robotic arms; an electronic library database of minimanipulations; a robotic planning module configured for real-time planning and adjustment based at least in part on the sensor data received from the one or more sensors in an electronic multi-stage process file, the electronic multi-stage process recipe file including a sequence of minimanipulations and associated timing data; a robotic interpreter module configured for reading the minimanipulation steps from the minimanipulation library and converting to a machine code; and a robotic execution module configured for executing the minimanipulation steps by the robotic platform to accomplish a functional result.

Grasping of an object, by an end effector of a robot, based on a grasp strategy that is selected using one or more machine learning models. The grasp strategy utilized for a given grasp is one of a plurality of candidate grasp strategies. Each candidate grasp strategy defines a different group of one or more values that influence performance of a grasp attempt in a manner that is unique relative to the other grasp strategies. For example, value(s) of a grasp strategy can define a grasp direction for grasping the object (e.g., “top”, “side”), a grasp type for grasping the object (e.g., “pinch”, “power”), grasp force applied in grasping the object, pre-grasp manipulations to be performed on the object, and/or post-grasp manipulations to be performed on the object.

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. In one embodiment, a robotic control platform, comprises one or more sensors; a mechanical robotic structure including one or more end effectors, and one or more robotic arms; an electronic library database of minimanipulations; a robotic planning module configured for real-time planning and adjustment based at least in part on the sensor data received from the one or more sensors in an electronic multi-stage process file, the electronic multi-stage process recipe file including a sequence of minimanipulations and associated timing data; a robotic interpreter module configured for reading the minimanipulation steps from the minimanipulation library and converting to a machine code; and a robotic execution module configured for executing the minimanipulation steps by the robotic platform to accomplish a functional result.

Methods, apparatus, and computer-readable media for determining and utilizing corrections to robot actions. Some implementations are directed to updating a local features model of a robot in response to determining a human correction of an action performed by the robot. The local features model is used to determine, based on an embedding generated over a corresponding neural network model, one or more features that are most similar to the generated embedding. Updating the local features model in response to a human correction can include updating a feature embedding, of the local features model, that corresponds to the human correction. Adjustment(s) to the features model can immediately improve robot performance without necessitating retraining of the corresponding neural network model.

Methods, systems, and apparatus, including computer-readable media storing executable instructions, for enhancing robot learning. In some implementations, a robot stores first embeddings generated using a first machine learning model, and the first embeddings include one or more first private embeddings that are not shared with other robots. The robot receives a second machine learning model from a server system over a communication network. The robot generates a second private embedding for each of the one or more first private embeddings using the second machine learning model. The robot adds the second private embeddings to the cache of the robot and removes the one or more first private embeddings from the cache of the robot.

The present invention relates to a method, such as a surgical method for assisting a surgeon for placing screws in the spine using a robot attached to a passive structure. The present invention also related to a method, such as a surgical method for assisting a surgeon for removing volumes in the body of a patient using a robot attached to a passive structure and to a device to carry out said methods. The present invention further concerns a device suitable to carry out the methods according to the present invention.

In an apparatus for controlling the positioning of an object having a specified point, a first limiter establishes a movable range of the specified point of the object, and limits movement of the specified point of the object within the movable range. The movable range includes a line connecting the position of the specified point of the object and a target position for the specified point of the object. A second limiter limits change of a specified orientation of the object from increasing an angular difference between the specified orientation of the object a target orientation for the specified orientation of the object.

Disclosed is a stocker for receiving a cassette. The stocker includes a shelf for receiving a cassette, a stocker robot, a teaching jig, and a teaching unit. The stocker robot includes a robot arm configured to load the cassette in the shelf and to unload the cassette from the shelf. The teaching jig is disposed in the shelf to teach the stocker robot. The teaching unit is disposed on the robot arm to acquire information for teaching the stocker robot using the teaching jig.

A device for robotic direct lead-through teaching includes a robot, a replacing member and a lead-through teaching member. The robot has an operation member coupled with the replacing member. The lead-through teaching member mounted replaceably at the replacing member has a force sensor. The force sensor has six-axis load information. A path teaching is executed manually upon the operation member of the robot so as to store coordinate information. In additional, a method for robotic direct lead-through teaching is also provided.