Abstract: 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.

Abstract: A controller for controlling an operation of a robot to execute a task is provided. The controller comprises a memory configured to store a set of dynamic movement primitives (DMPs) associated with the task. The set of DMPs comprise a set of at least two dynamical systems: a function representing point attractor dynamics and a forcing function corresponding to a learned demonstration of the task. The controller comprises a processor configured to transform the set of DMPs to a set of constrained DMPs (CDMPs) by determining a perturbation function associated with the forcing function. The perturbation function is associated with a set of operational constraints. The processor is further configured to solve, a non-linear optimization problem for the set of CDMPs based on the set of operational constraints and generate, a control input for controlling the robot for executing the task, based on the solution.

Abstract: A controller controls a motion of an object performing a task for changing a state of the object from a start state to an end state while avoiding collision of the object with an obstacle according to an optimal trajectory determined by solving an optimization problem of the dynamics of the object producing an optimal trajectory for performing the task subject to constraints on a solution of first-order stationary conditions modeling a minimum distance between the convex hull of the object and the convex hull of the obstacle using complementarity constraints.

Abstract: The present disclosure provides a system and a method for controlling a motion of a robot from a starting point to a target point within a bounded space with a floorplan including one or multiple obstacles. The method includes solving for an electric potential in a bounded virtual space formed by scaling the floorplan of the bounded space with the one or multiple obstacles and applying charge to at least one bound of the bounded virtual space while treating the scaled obstacles as metallic surfaces with a constant potential value, wherein the electric potential provides multiple equipotential curves within the bounded virtual space. The method further includes selecting an equipotential curve with a potential value different from a potential value of an obstacle equipotential curve, determining a motion path based on the selected equipotential curve, and controlling the motion of the robot based on the determined motion path.

Abstract: A manipulation controller is provided for reorienting an object by a manipulator of a robotic system. The manipulation controller includes an interface controller configured to acquire measurement data from sensors arranged on the robotic system, at least one processor, and a memory configured to store a computer-implemented method.

Abstract: A robotic system for manipulating an object with a robotic manipulator is provided. The robotic system is configured to collect a digital representation of a task for manipulating the object; solve a robust control problem to optimize a sequence of control forces to be applied by the robotic manipulator to change a state of the object, where an evolution of the state of the object is governed by a stochastic complementarity system modeling the task with a predefined probability. The robust control problem optimizes a cost function to generate the sequence of control forces performing the task subject to joint chance constraints including a first chance constraint on the state of the object being manipulated and a second chance constraint on stochastic complementarily constraints modeling manipulation of the object. The robotic system is further configured to control the manipulation of the object based on the sequence of control forces.

Abstract: A controller is provided for generating a policy controlling a system by learning a dynamics of the system.

Abstract: A robot for performing an assembly operation is provided. The robot comprises a processor configured to determine a control law for controlling a plurality of motors of the robot to move a robotic arm according to an original trajectory, execute a self-exploration program to produce training data indicative of a space of the original trajectory, and learn, using the training data, a non-linear compliant control law including a non-linear mapping that maps measurements of a force sensor of the robot to a direction of corrections to the original trajectory defining the control law. The processor transforms the original trajectory according to a new goal pose to produce a transformed trajectory, update the control law according to the transformed trajectory to produce the updated control law, and command the plurality of motors to control the robotic arm according to the updated control law corrected with the compliance control law.

Abstract: A computer-implemented learning method for optimizing a control policy controlling a system is provided. The method includes receiving states of the system being operated for a specific task, initializing the control policy as a function approximator including neural networks, collecting state transition and reward data using a current control policy, estimating an advantage function and a state visitation frequency based on the current control policy, updating the current control policy using the second-order approximation of the objective function, a second-order approximation of the KL-divergence constraint on the permissible change in the policy using a quasi-newton trust region policy optimization, and determining an optimal control policy, for controlling the system, based on the average reward accumulated using the updated current control policy.

Abstract: A controller controls a motion of an object performing a task for changing a state of the object from a start state to an end state while avoiding collision of the object with an obstacle according to an optimal trajectory determined by solving an optimization problem of the dynamics of the object producing an optimal trajectory for performing the task subject to constraints on a solution of first-order stationary conditions modeling a minimum distance between the convex hull of the object and the convex hull of the obstacle using complementarity constraints.

Abstract: A system for performing a task according to a reference trajectory is provided. The system includes at least one actuator configured to change a state of the system according to a control input, and a memory configured to store a model of dynamics of the system including a known part of the dynamics of the system as a function of the state of the system and the control input to the system and an unknown part of the dynamics of the system as a function of the state of the system, wherein the unknown part of the dynamics of the system is represented by parameters of a probabilistic distribution including a first-order moment and a second-order moment of the probabilistic distribution. The system also includes a control system configured to recursively determine and submit the control input to the actuator to change the state of the system.

Abstract: A robot for performing an assembly operation is provided. The robot comprises a processor configured to determine a control law for controlling a plurality of motors of the robot to move a robotic arm according to an original trajectory, execute a self-exploration program to produce training data indicative of a space of the original trajectory, and learn, using the training data, a non-linear compliant control law including a non-linear mapping that maps measurements of a force sensor of the robot to a direction of corrections to the original trajectory defining the control law. The processor transforms the original trajectory according to a new goal pose to produce a transformed trajectory, update the control law according to the transformed trajectory to produce the updated control law, and command the plurality of motors to control the robotic arm according to the updated control law corrected with the compliance control law.

Abstract: A manipulator learning-control apparatus for controlling a manipulating system that includes an interface configured to receive manipulator state signals of the manipulating system and object state signals with respect to an object to be manipulated by the manipulating system in a workspace, wherein the object state signals are detected by at least one object detector, an output interface configured to transmit initial and updated policy programs to the manipulating system, a memory to store computer-executable programs including a data preprocess program, object state history data, manipulator state history data, a Derivative-Free Semi-parametric Gaussian Process (DF-SPGP) kernel learning program, a Derivative-Free Semi-parametric Gaussian Process (DF-SPGP) model learning program, an update-policy program and an initial policy program, and a processor, in connection with the memory, configured to transmit the initial policy program to the manipulating system for initiating a learning process that operates th

Abstract: A system for controlling a robotic arm performing insertion of a component along an insertion line accepts measurements of force experienced by the wrist of robotic arm at current position along insertion line and determines probability of value of the force conditioned on the current value of the position according to a probabilistic relationship for the force experienced by the wrist of the robotic arm along the insertion line as a probabilistic function of the positions of the wrist of the robotic arm along the line of insertion. The probabilistic function is learned from measurements of the operation repeatedly performed by one or multiple robotic arms having the configuration of the robotic arm under the control. The system determines a result of anomaly detection based on the probability of the current value of the force and controls the robotic arm based on the result of anomaly detection.

Abstract: A controller for optimizing a local control policy of a system for trajectory-centric reinforcement learning is provided. The controller includes performing steps of learning a stochastic predictive model for the system using a set of data collected during trial and error experiments performed using an initial random control policy, estimating mean prediction and uncertainty associated, determining a local set of deviations of the system using the learned stochastic system model, from a nominal system state upon use of a control input at a current time-step, determining a system state with a worst-case deviation, determining a gradient of the robustness constraint, providing and solving a robust policy optimization problem using non-linear programming to obtain system trajectory and stabilizing local policy simultaneously, updating the control data according to the solved optimization problem, and output the updated control data via the interface.

Abstract: A system evaluates a plurality of faults in an operation of a machine at a set of future instances of time. The system uses a neural network including a first subnetwork sequentially connected with a sequence of second subnetworks for each of the future instance of time such that an output of one subnetwork is an input to a subsequent subnetwork. The first subnetwork accepts the current time-series data and the current setpoints of operation of the machine. Each of the second subnetworks accepts the output of a preceding subnetwork, an internal state of the preceding subnetwork, and a future setpoint for a corresponding future instance of time. Each of the second subnetworks outputs an individual prediction of each fault of a plurality of faults at the corresponding future instance of time.

Abstract: A computer-implemented learning method for optimizing a control policy controlling a system is provided. The method includes receiving states of the system being operated for a specific task, initializing the control policy as a function approximator including neural networks, collecting state transition and reward data using a current control policy, estimating an advantage function and a state visitation frequency based on the current control policy, updating the current control policy using the second-order approximation of the objective function, a second-order approximation of the KL-divergence constraint on the permissible change in the policy using a quasi-newton trust region policy optimization, and determining an optimal control policy, for controlling the system, based on the average reward accumulated using the updated current control policy.

Abstract: A manipulator learning-control apparatus for controlling a manipulating system that includes an interface configured to receive manipulator state signals of the manipulating system and object state signals with respect to an object to be manipulated by the manipulating system in a workspace, wherein the object state signals are detected by at least one object detector, an output interface configured to transmit initial and updated policy programs to the manipulating system, a memory to store computer-executable programs including a data preprocess program, object state history data, manipulator state history data, a Derivative-Free Semi-parametric Gaussian Process (DF-SPGP) kernel learning program, a Derivative-Free Semi-parametric Gaussian Process (DF-SPGP) model learning program, an update-policy program and an initial policy program, and a processor, in connection with the memory, configured to transmit the initial policy program to the manipulating system for initiating a learning process that operates th

Abstract: A control system for controlling a machine with partially modeled dynamics to perform a task estimates a Lipschitz constant bounding the unmodeled dynamics of the machine, initializes a constraint-admissible control policy using the Lipschitz constant for controlling the machine to perform a task, such that the constraint-admissible control policy satisfies stability constraint, safety and admissibility constraint including one or combination of a state constraint and an input constraint, and has a finite cost on the performance of the task, and jointly controls the machine and update the control policy to control an operation of the machine to perform the task according the control policy starting with the initialized constraint-admissible control policy and to update the control policy using data collected while performing the task. In such a manner, the updated control policy is constraint-admissible.

Abstract: A control system for controlling a machine with partially modeled dynamics to perform a task estimates a Lipschitz constant bounding the unmodeled dynamics of the machine, initializes a constraint-admissible control policy using the Lipschitz constant for controlling the machine to perform a task, such that the constraint-admissible control policy satisfies stability constraint, safety and admissibility constraint including one or combination of a state constraint and an input constraint, and has a finite cost on the performance of the task, and jointly controls the machine and update the control policy to control an operation of the machine to perform the task according the control policy starting with the initialized constraint-admissible control policy and to update the control policy using data collected while performing the task. In such a manner, the updated control policy is constraint-admissible.