Mitsubishi Electric Research Laboratories, Inc.

Mitsubishi Electric Corporation

Radu Ioan Corcodel

Jeroen Van Baar

Siddarth Jain

Daniel Nikolaev Nikovski

Mouhacine Benosman

Arvind Raghunathan

Alan Sullivan

Devesh Jha

Diego Romeres

Esra Cansizoglu

Aykut Onol

Diogo Rodrigues Marcal De Almeida

Seiji Shaw

Aziz Kocanaogullari

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 system for parameter tuning for robotic manipulators is provided. The system includes an interface configured to receive a task specification, a plurality of physical parameters, and a plurality of control parameters, wherein the interface is configured to communicate with a real-world robot via a robot controller. The system further includes a memory to store computer-executable programs including a robot simulation module, a robot controller, and an auto-tuning module a processor, in connection with the memory. In this case, the processor is configured to acquire, in communication with the real-world robot, state values of the real-world robot, state values of the robot simulation module, simultaneously update, by use of a predetermined optimization algorithm with the auto-tuning module, an estimate of one or more of the physical, and said control parameters, and store the updated parameters.

Simulation-in-the-loop tuning of robot parameters for system modeling and control

A controller is provided for interactive classification and recognition of an object in a scene using tactile feedback. The controller includes an interface configured to transmit and receive the control, sensor signals from a robot arm, gripper signals from a gripper attached to the robot arm, tactile signals from sensors attached to the gripper and at least one vision sensor, a memory module to store robot control programs, and a classifier and recognition model, and a processor to generate control signals based on the control program and a grasp pose on the object, configured to control the robot arm to grasp the object with the gripper. Further, the processor is configured to compute a tactile feature representation from the tactile sensor signals and to repeat gripping the object and computing a tactile feature representation with the set of grasp poses, after which the processor, processes the ensemble of tactile features to learn a model which is utilized to classify or recognize the object as known or unknown.

Interactive tactile perception method for classification and recognition of object instances

An apparatus and a method for planning contact-interaction trajectories are provided. The apparatus is a robot that accepts contact interactions between the robot and the environment. The robot stores a dynamic model representing geometric, dynamic, and frictional properties of the robot and the environment, and a relaxed contact model to representing dynamic interactions between the robot and the object via virtual forces. The robot further determines, iteratively until a termination condition is met, a trajectory, associated control commands for controlling the robot, and virtual stiffness values by performing optimization reducing stiffness of the virtual force and minimizing a difference between the target pose of the object and a final pose of the object moved from the initial pose. Further, an actuator moves a robot arm of the robot according to the trajectory and the associated control commands.

Apparatus and method for planning contact-interaction trajectories

An object detection system for detecting and manipulating objects on a workspace includes a three dimensional (3D) sensor configured to acquire and transmit point clouds of a scene, each point cloud including one or more objects in the workspace, manipulator configured to move or grip each of the one or more objects, a memory to store the images and a computer executable program including an object detection-localization program, a segmentation program, a gripping-moving program and a geometry reconstruction program, a processor to perform the computer executable program using the images in connection with the 3D sensor, the memory and the manipulator. The computer executable program is configured to cause the processor to perform steps of generating frames including the point clouds and measurements, generating an initial map from a first frame, moving part of the one or more objects by using the manipulator using the first image, acquiring, after the moving step, a second frame, generating a first dynamic map and a static map from the initial map using the second frame, moving another part of the one or more objects based on the first dynamic map, splitting at least one dynamic map among the first dynamic map and the second dynamic maps when the at least one dynamic map includes a segment being outlier with respect to the one or more objects, reconstructing a 3D geometry of the one or more objects from the split map, and determining when the one or more objects are separated from each other.

Detection, tracking and 3D modeling of objects with sparse RGB-D SLAM and interactive perception

A system for active-training a neural network includes an input interface to receive a set of images, a memory to store an active sampler, a feature generator and a subset of the images selected from the set of the images, the subset of the images having similarity values based on a predetermined feature domain information, at least one processor to train the feature generator by use of the active sampler. The active sampler is configured to perform first-selecting, from the subset of the images, a pair of images as matching pair images according to a threshold of similarity values, second-selecting a pair of images from another subset of the images, feeding the matching pair images and the unmatched pair images to the feature generator, updating weighting parameters and increasing the threshold according to preset values an output interface to render the weighting parameters of the feature generator.

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Quincy, MA, United States of America

Radu Ioan Corcodel