Siemens Aktiengesellschaft

University of California

Siemens Aktiengesell Schaft

Chengtao Wen

Juan L Aparicio Ojea

Eugen Solowjow

Heiko Claussen

Martin Sehr

Max A Fahrenkopf

Richard Gary McDaniel

Mathias Maurmaier

Tao Cui

Jianlan Luo

Swen Elpelt

Mohamed El Amine Houyou

Xiaowen Yu

Autonomous operations, such as robotic grasping and manipulation, in unknown or dynamic environments present various technical challenges. For example, three-dimensional (3D) reconstruction of a given object often focuses on the geometry of the object without considering how the 3D model of the object is used in solving or performing a robot operation task. As described herein, in accordance with various embodiments, models are generated of objects and/or physical environments based on tasks that autonomous machines perform with the objects or within the physical environments. Thus, in some cases, a given object or environment may be modeled differently depending on the task that is performed using the model. Further, portions of an object or environment may be modeled with varying resolutions depending on the task associated with the model.

Task-oriented 3D reconstruction for autonomous robotic operations

Systems and methods for controlling robots including industrial robots. A method includes executing () a program () to control a robot () by the robot control system (). The method includes receiving () robot state information (). The method includes receiving () force torque feedback () inputs from a sensor () on the robot (). The method includes producing () a robot control command for the robot () based on the robot state information () and the force torque feedback () inputs. The method includes controlling () the robot () using the robot control command.

Reinforcement learning for contact-rich tasks in automation systems

Examples of techniques for threat detection in an industrial process system are described herein. An aspect includes determining a plurality of subsystems of an industrial process system. Another aspect includes, for each of the plurality of subsystems, constructing and training a respective deep autoencoder (DAE) model of the subsystem based on data corresponding to the industrial process system. Another aspect includes monitoring the industrial process system using the plurality of DAE models corresponding to the plurality of subsystems. Another aspect includes, based on the plurality of DAE models, determining a cyberattack in a subsystem of the plurality of subsystems.

Industrial process system threat detection

For manufacturing process control, closed-loop control is provided () based on a constrained reinforcement learned network (). The reinforcement is constrained () to account for the manufacturing application. The constraints may be for an amount of change, limits, or other factors reflecting capabilities of the controlled device and/or safety.

Manufacturing process control using constrained reinforcement machine learning

A system controller for visual servoing includes a technology module with dedicated hardware acceleration for deep neural network that retrieves a desired configuration of a workpiece object being manipulated by a robotic device and receives visual feedback information from one or more sensors on or near the robotic device that includes a current configuration of the workpiece object. The hardware accelerator executes a machine learning model trained to process the visual feedback information and determine a configuration error based on a difference between the current configuration of the workpiece object and the desired configuration of the workpiece object. A servo control module adapts a servo control signal to the robotic device for manipulation of the workpiece object in response to the configuration error.

Machine learning enabled visual servoing with dedicated hardware acceleration

Computerized system and method are provided. A robotic manipulator () is arranged to grasp objects (). A gripper () is attached to robotic manipulator (), which includes an imaging sensor (). During motion of robotic manipulator (), imaging sensor () is arranged to capture images providing different views of objects in the environment of the robotic manipulator. A processor () is configured to find, based on the different views, candidate grasp locations and trajectories to perform a grasp of a respective object in the environment of the robotic manipulator. Processor () is configured to calculate respective values indicative of grasp quality for the candidate grasp locations, and, based on the calculated respective values indicative of grasp quality for the candidate grasp locations, processor () is configured to select a grasp location likely to result in a successful grasp of the respective object.

Computerized system and method using different image views to find grasp locations and trajectories for robotic pick up

Systems and methods for automatic generation of robot control policies include a CAD-based simulation engine for simulating CAD-based trajectories for the robot based on cost function parameters, a demonstration module configured to record demonstrative trajectories of the robot, an optimization engine for optimizing a ratio of CAD-based trajectories to demonstrative trajectories based on computation resource limits, a cost learning module for learning cost functions by adjusting the cost function parameters using a minimized divergence between probability distribution of CAD-based trajectories and demonstrative trajectories; and a deep inverse reinforcement learning engine for generating robot control policies based on the learned cost functions.

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Redwood City, CA, United States of America

Chengtao Wen