A robotic controller is provided for generating sequences of movement primitives for sequential tasks of a robot having a manipulator. The controller includes at least one control processor, and a memory circuitry storing a dictionary including the movement primitives, a pretrained learning module, and a graph-search based planning module having instructions stored thereon. The controller to perform steps acquiring a planned task provided by an interface device operated by a user, wherein the planned task is represented by an initial state and a goal state with respect to an object, generating a planning graph by searching a feasible path of the object for the novel task using the graph-search based planning module and selecting movement primitives from the dictionary in the pretrained learning module, wherein the pretrained learning module has been trained based on demonstration tasks, parameterizing the feasible path represented by the movement primitives as dynamic movement primitives (DMPs) using the initial state and goal state, and implementing the parameterized feasible path as a trajectory according to the selected movement primitives using the manipulator of the robot by tracking and following the parameterized for the planned task.

A robot intelligence engine receives highly immersive virtual environment (HIVE) data characterizing a set of robot tasks executed by a test robot in a HIVE, wherein the robot tasks of the set of robot tasks include a robot skill. The robot intelligence engine receives sensor data from a problem detecting robot deployed in an environment of operation that characterizes conditions corresponding to a detected problem and searches the set of robot tasks to identify a subset of the robot tasks that are potentially employable to remedy the detected problem. The robot intelligence engine simulates the subset of robot tasks to determine a likelihood of success for the subset of robot tasks. The simulation generates a set of unsupervised robot tasks that are potentially employable to remedy the detected problem. The robot intelligence engine selects one of the subset of robot tasks or one of the unsupervised robot tasks.

Robots, systems, methods, and computer program products for training and operating (semi-)autonomous robots to complete work objectives are described. A robot accesses a library of reusable work primitives from a catalog of libraries of reusable work primitives, each reusable work primitive corresponding to a respective basic sub-action that the robot is trained to autonomously perform. A work objective is analyzed to determine a sequence of reusable work primitives that complete the work objective, and the robot executes the sequence to complete the work objective. A robot can be deployed with access to an appropriate library of reusable work primitives, based on expectations for the robot. The robot is trained to perform reusable work primitives in multiple libraries, by generating control instructions which cause the robot to perform each reusable work primitive. Training is performed by real-world robots performing reusable work primitives, or simulated robot instances performing the reusable work primitives.

The present disclosure provides a motion control method and apparatus, a robot and a non-transitory storage medium. The method includes: obtaining a jump parameter of the robot, the jump parameter including an expected velocity and an expected jump height before take-off of the robot; obtaining a jump trajectory corresponding to the jump parameter in a preset action library, the action library including a jump trajectory that is marked with a corresponding jump parameter; and controlling the robot to reach the expected velocity, and controlling the robot to jump according to the jump trajectory.