Mitsubishi Electric Research Laboratories, Inc.

Mitsubishi Electric Corporation

Diego Romeres

Devesh Jha

Daniel Nikolaev Nikovski

Arvind Raghunathan

Emil Laftchiev

Yuki Shirai

Alberto Dalla Libera

Stefano Di Cairano

William Yerazunis

Mouhacine Benosman

Rien Quirynen

Ankush Chakrabarty

Chiori Hori

Radu Ioan Corcodel

Ruggero Carli

Xiang Zhang

Seiji Shaw

Shivam Vats

Lingfeng Sun

Fabio Amadio

Riccardo Antonello

Patrik Kolaric

A controller is provided for manipulating objects by a robot arm having a gripper. The controller includes a large language model (LLM) planner configured to acquire the states and the task description and generate an action sequence command that operates the robot arm with the gripper based on the task description, the current observations, historical information including historical actions and historical observations from previous steps. The controller includes a robot interface configured to generate and transmit commands to operate the robot arm in response to the action sequence from the LLM planner and an LLM evaluator configured to acquire the states, the current observations, analyze the historical information including historical actions and historical observations from previous steps, evaluate missing information required to complete the task, evaluate new information acquired by the sensors, estimate a task-execution status after the robot arm operated the generated action sequence, and output updated information to the LLM planner.

System and method for robotic planning in partially observable environments using large language models

A feedback controller for controlling a robotic manipulator to perform a task is provided. The feedback controller comprises a memory configured to store a hierarchical reinforcement learning (HRL) neural network including (i) nominal control policy, and (ii) recovery control policy. The recovery control policy is trained based on a recovery policy reward to select a switch-to-nominal action to transfer control to the nominal control policy to perform the task. The recovery policy reward is dependent on a nominal policy reward for training the nominal control policy. The feedback controller further comprises a processor configured to iteratively execute the HRL neural network to select at least one of a nominal action based on the nominal control policy, and a recovery action based on the recovery control policy and to control the robotic manipulator to perform the task based on the selected action.

System and method for controlling a robotic manipulator based on hierarchical reinforcement learning of control policies

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.

System and method for learning sequences in robotic tasks for generalization to new tasks

A controller for controlling robotic manipulator according to a task is provided. The controller is to collect data relating to a state and an object property of an object, and execute a state adapter model to produce a state correction to state of the object having the object property different from a unitary property of a unitary object. The controller is to execute a control policy using the state correction to produce an action for the unitary object, and execute an action adapter model to produce an action correction to the action produced by the control policy. The state correction and action correction are produced based on difference between object property and unitary property. The control policy is to map a state of the unitary object to the action of the robotic manipulator to manipulate the unitary object according to the task.

System and method for controlling a robotic manipulator

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. The instructions of the method include acquiring measurement data from vision sensors and force sensors arranged on the robotic system, determining an input-output relation for the object based on a nonlinear static model representing input-output relationships between contact forces and movements of the object on the workbench, representing interaction between the object and the manipulator using complementarity constraints to capture the contact state between the object and the manipulator, formulating a representation for frictional stability of the object based on the non-linear static model at the external contacts with the workbench; formulating a bilevel optimization problem so as to maximize the frictional stability over a position trajectory of the object being manipulated on the workbench, estimating uncertainty value in physical parameters to be compensated by performing the bilevel optimization problem, solving the bilevel optimization problem using the non-linear optimization solver and generating control data with respect to a sequence of the contact forces being applied to the object by using the manipulator.

System and method for robust pivoting for re-orienting parts during robotic assembly

A method for controlling a system by a controller comprises accepting a current state of the system and selecting, using a trained function of the current state, a solver from a set of solvers. The method further comprises solving an optimal control optimization problem using the selected solver to produce a current control input, such that for at least some different control steps, the predictive controller solves a formulation of the optimal control optimization problem with different solvers having different accuracies, requiring different computational resources, or both and submitting the current control input to the system thereby changing the current state of the system.

Apparatuses systems and methods for optimization-based control of physical systems

A controller for controlling a system that includes a policy to control the system is provided. The controller includes an interface connected to the system, the interface acquires an action state and a measurement state via sensors measuring the system, a memory to store computer-executable program modules including a model learning module and a policy learning module, a processor that performs steps of the program modules. The steps include offline-modeling to generate offline-learning states based on the action state and measurement state using the model learning program, providing the offline states to the policy learning program to generate policy parameters, and updating the policy of the system to operate the system based on the policy parameters.

Method and system for modelling and control partially measurable systems

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.

System and method for controlling a robot using constrained dynamic movement primitives

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.

System and method for robust robotic manipulation using chance constrained optimization

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.

Object manipulation with collision avoidance using complementarity constraints

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.

System and method for robotic assembly based on adaptive compliance

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.

System and method for policy optimization using quasi-Newton trust region method

A system for detecting an anomaly in an execution of a task in mixed human-robot processes. Receiving human worker (HW) signals and robot signals. A processor to extract from the HW signals, task information, measurements relating to a state of the HW, and input into a Human Performance (HP) model, to obtain a state of the HW based on previously learned boundaries of the state of the HW, the state of the HW is then inputted into a Human-Robot Interaction (HRI) model, to determine a classification of an anomaly or no anomaly. Update HRI model with robot operation signals, HW signals and classified anomaly, determine a control action of a robot interacting with the HW or a type of an anomaly alarm using the updated HRI model and classified anomaly. Output the control action of the robot to change a robot action or output the type of the anomaly alarm.

Systems and methods automatic anomaly detection in mixed human-robot manufacturing processes

A system for detection of an anomaly in a discrete manufacturing process (DMP) with human-robot teams executing a task. Receive signals including robot, worker and DMP signals. Predict a sequence of events (SOEs) from DMP signals. Determine whether the predicted SOEs in the DMP signals is inconsistent with a behavior of operation of the DMP described in a DMP model, and if the predicted SOEs from DMP signals is inconsistent with the behavior, then an alarm is to be signaled. Input worker data into a Human Performance (HP) model, to obtain a state of the worker based on previously learned boundaries of human state. The state of the HW is then input into the HRI model and the DMP model to determine a classification of anomaly or no anomaly. Update a Human-Robot Interaction (HRI) model to obtain a control action of a robot or a type of an anomaly alarm.

Systems and methods for advance anomaly detection in a discrete manufacturing process with a task performed by a human-robot team

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 the manipulator system manipulating the object while a preset period of time.

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Somerville, MA, United States of America

Diego Romeres