A system includes a first robotic machine having a first set of capabilities for interacting with a target object on stationary equipment; a second robotic machine having a second set of capabilities for interacting with the target object; and a task manager that can determine capability requirements to perform a task on the target object. The task has an associated series of sub-tasks. The task manager can assign a first sequence of sub-tasks for performance by the first robotic machine based on the first set of capabilities and a second sequence of sub-tasks for performance by the second robotic machine based on the second set of capabilities. The first and second robotic machines can coordinate performance of the first sequence of sub-tasks by the first robotic machine with performance of the second sequence of sub-tasks by the second robotic machine to accomplish the task.

An improved method, system, and apparatus is provided to implement a general architecture for robot systems. A mode execution module is provided to universally execute execution modes on different robotic system. A system includes an execution module that receives software instructions in a normalized programming language. The system also includes an interface having a translation layer that converts the software instructions from the normalized language into robot-specific instructions that operate in a particular robotic system. The system further includes a controller that is communicatively coupled to the interface, wherein the controller receives the robot-specific instructions. Moreover, the system includes a robotic device that is operatively controlled by the controller by execution of the robot-specific instructions.

A method and system for robotic motion planning which perform dynamic velocity attenuation to avoid robot collision with static or dynamic objects. The technique maintains the planned robot tool path even when speed reduction is necessary, by providing feedback of a computed slowdown ratio to a tracking controller so that the path computation is always synchronized with current robot speed. The technique uses both robot-obstacle distance and relative velocity to determine when to apply velocity attenuation, and computes a joint speed limit vector based on a robot-obstacle distance, a maximum obstacle speed, and a computed stopping time as a function of the joint speed. Two different control structure implementations are disclosed, both of which provide feedback of the slowdown ratio to the motion planner as needed for faithful path following. A method of establishing velocity attenuation priority in multi-robot systems is also provided.

In an embodiment, a method and system use various sensors to determine a shape of a collection of materials (e.g., foodstuffs). A controller can determine a trajectory which achieves the desired end-state, possibly chosen from a set of feasible, collision-free trajectories to execute, and a robot executes that trajectory. The robot, executing that trajectory, scoops, grabs, or otherwise acquires the desired amount of material from the collection of materials at a desired location. The robot then deposits the collected material in the desired receptacle at a specific location and orientation.

Methods, systems, and robots for multi-layer prepreg composite sheet layup. The method includes obtaining a dataset including start and end point pairs of a mold of the 3D part. The method includes generating a layup sequence based on the dataset and generating multiple trajectories for one or more movements of the first robot or the first robot arm based on the layup sequence. The method includes causing a second robot or a second robot art to hold or grasp the prepreg layer or sheet a threshold distance above the mold or the 3D part. The method includes causing the first robot or the first robot arm to place or conform the prepreg layer or sheet to the mold of the 3D part.

A cooperative robotic arm system includes a first robotic arm, a second robotic arm and a controller. The first robotic arm has first working vector. The second robotic arm has second working vector. The controller is configured to: (1) control the first robotic arm and the second robotic arm to stop moving; (2) determine whether a first projection vector of the first working vector projected on a first coordinate axis and a second working vector projected on the first coordinate axis overlaps; (3) when they overlap, determine whether a third projection vector of the first working vector projected on a second coordinate axis and a fourth projection vector of the second working vector projected on the second coordinate axis overlap; and, (4). when they do no overlap, control a controlled-to-moved one of the first robotic arm and the second robotic arm to move along a reset path.

The present invention relates to systems and methods for accounting for edge cases (i.e. tail data) in automated decision making systems, for example automated robotic picking systems. The systems and methods provide for retraining machine learning (ML) models so that the edge cases can be handled in a manner that requires less (or no) human intervention. The disclosed systems and methods create updated ML models, replacement ML models, and/or supplementary ML models that can provide better performance (e.g. improved automated robotic picking) when edge cases are encountered. Furthermore, the present inventions disclose systems and methods for obtaining training data faster and in a more cost effective manner, which enables the systems and methods disclosed herein to update models at a faster rate, thereby enabling broader, system-wide handling of edge cases in a more effective and efficient manner.

System and techniques for cumulative learning robot execution plan generation are described herein. A first execution plan report based on execution of a first execution plan by a first robot may be received. Here, the first execution plan report includes a first metric for a first operation of the first execution plan. A second execution plan report based on execution of a second execution plan by a second robot may also be received that includes a second metric for a second operation of the second execution plan. Here, the second operation corresponds to the first operation. The first metric and the second metric are analyzed to determine that the second operation is an improvement to the first operation. Then, a modified first execution plan that replaces the first operation with the second operation may be transmitted to the first robot.

A system includes a first robotic machine having a first set of capabilities for interacting with a target object on stationary equipment; a second robotic machine having a second set of capabilities for interacting with the target object; and a task manager that can determine capability requirements to perform a task on the target object. The task has an associated series of sub-tasks. The task manager can assign a first sequence of sub-tasks for performance by the first robotic machine based on the first set of capabilities and a second sequence of sub-tasks for performance by the second robotic machine based on the second set of capabilities. The first and second robotic machines can coordinate performance of the first sequence of sub-tasks by the first robotic machine with performance of the second sequence of sub-tasks by the second robotic machine to accomplish the task.

An automated method determines a safety zone for a robot. The robot carries out operations along a specified trajectory. For collision-free operation, a safety zone is determined by: dividing the specified trajectory into a plurality of subtrajectories; determining a plurality of fine-grained envelope cuboids around extreme points of each subtrajectory; and determining a number of optimized envelope cuboids from an enlargement of individual fine-grained envelope cuboids in relation to the volume occupied by the enlarged fine-grained envelope cuboids. The optimized envelope cuboids determined in this way form the safety zone for the trajectory. This automated method can be expanded to multiple trajectories of a robot, multiple robots, and replanning a trajectory for an occupied semaphore zone.