To provide a robot system equipped with an off-line programming device which can create a machining path consisting of a trajectory of any shape, and can reduce the workload required in teaching of the machining path of any shape. Provided is an off-line programming device which teaches a program of a robot equipped with a toll that machines a workpiece arranged within a work space, the off-line programming device including a trajectory information creation unit which creates trajectory information of any 3D shape consisting of at least one layer; and a trajectory information editing unit which edits line segments constituting the trajectory information of the any 3D shape consisting of at least one layer.

A system for robot and human collaboration. The system comprises: a multi-axis robot; one or more torque sensors, each torque sensor being configured to measure a torque about a respective axis of the multi-axis robot; and a controller configured to: receive one or more torque measurements taken by the one or more torque sensors; compare the one or more torque measurements or a function of the one or more torque measurements to a threshold value; and control the multi-axis robot based on the comparison.

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.

Robotic systems, methods of operation of robotic systems, and storage media including processor-executable instructions are disclosed herein. The system may include a robot, at least one processor in communication with the robot, and an operator interface in communication with the robot and the at least one processor. The method may include executing a first set of autonomous robot control instructions which causes a robot to autonomously perform the at least one task in an autonomous mode, and generating a second set of autonomous robot control instructions from the first set of autonomous robot control instructions and a first set of environmental sensor data received from a senor. The second set of autonomous robot control instructions when executed causes the robot to autonomously perform the at least one task. The method may include producing at least one signal that represents the second set of autonomous robot control instructions.

A motion trajectory planning method for a robotic manipulator having a visual inspection system, includes: in response to a command instruction, obtaining environmental data collected by the visual inspection system; determining an initial DS model motion trajectory of the robotic manipulator according to the command instruction, the environmental data, and a preset teaching motion DS model library, wherein the teaching motion DS model library includes at least one DS model motion trajectory generated based on human teaching activities; and at least based on a result of determining whether there is an obstacle, whose pose is on the initial DS model motion trajectory, in a first object included in the environmental data, correcting the initial DS model motion trajectory to obtain a desired motion trajectory of the robotic manipulator.

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 and systems for improved control of robotic arms are presented. In one embodiment, a method is presented that includes predefining a plurality of motion primitives, which may include one or more preconditions and effects. A target state for a plurality of workpieces may be determined as well as an initial state of the plurality of workpieces. A sequence of operations may be generated based on the preconditions and/or effects of the motion primitives, as well as the target state and the initial state. Executing the sequence of operations may be capable of changing the plurality of workpieces from the initial state to the target state.

Systems, devices, and methods for training and operating (semi-)autonomous robots to complete multiple different work objectives are described. A robot control system stores a library of reusable work primitives each corresponding to a respective basic sub-task or sub-action that the robot is operative to autonomously perform. A work objective is analyzed to determine a sequence (i.e., a combination and/or permutation) of reusable work primitives that, when executed by the robot, will complete the work objective. The robot executes the sequence of reusable work primitives to complete the work objective. The reusable work primitives may include one or more reusable grasp primitives that enable(s) a robot's end effector to grasp objects. Simulated instances of real physical robots may be trained in simulated environments to develop control instructions that, once uploaded to the real physical robots, enable such real physical robots to autonomously perform reusable work primitives.

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.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for providing user feedback for robotic demonstration learning. One of the methods includes initiating a local demonstration learning process to collect respective local demonstration data for each of one or more demonstration subtasks defined by a skill template to be executed by a robot. Local demonstration data is repeatedly collected for each of the one or more demonstration subtasks of the skill template while a user manipulates a robot to perform each of the one or more demonstration subtasks defined by the skill template. A respective progress value for each of the one or more demonstration subtasks defined by the skill template is maintained. A user interface presentation is generated that presents a suggested demonstration to be performed by the user based on a respective progress value for each demonstration subtask.