Robert Boschgmbh

International Business Machines Corporation

Zohar Feldman

Philipp Christian Schillinger

Anh Vien Ngo

Miroslav Gabriel

Alexander Kuss

Asser Nasreldin Tantawi

Malgorzata Steinder

Ian N Whalley

Diana Jeanne Arroyo

Dotan Di Castro

Michael Masin

Christian Daniel

Hanna Ziesche

Maksym Lefarov

Joel Oren

Felix Milo Richter

Yagil Engel

Nima Manafzadeh Dizbin

Ayal Taitler

A method for scheduling a set of jobs for a plurality of machines. Each job is defined by at least one feature which characterizes a processing time of the job. If any of the machines is free, a job from of the set of jobs is selected to be carrying out by said machine and scheduled for said machine. The job is selected as follows: a Graph Neural Network receives as input the set of jobs and a current state of at least the machine which is free, the Graph Neural Network outputs a reward for the set of jobs if launched on the machines, which states are inputted into the Graph Neuronal Network, and the job for the free machine is selected depending on the Graph Neural Network output.

Device and method for scheduling a set of jobs for a plurality of machines

A method for picking up an object by means of a robotic device. The method includes obtaining at least one depth image of the object; determining, for each of a plurality of points of the object, the value of a measure of the scattering of surface normal vectors in an area around the point of the object; supplying the determined values to a neural network configured to output, in response to an input containing measured scattering values, an indication of object locations for pick-up; determining a location of the object for pick-up from an output which the neural network outputs in response to the supply of the determined values; and controlling the robotic device to pick up the object at the determined location.

Method for picking up an object by means of a robotic device

A method for ascertaining object information from image data. The method includes training an agent with the aid of reinforcement learning, successively recording images according to actions that are output by the agent, after each recording, the agent obtaining information, generated from the previously recorded images, concerning the location of surface points of an object as state information, and ascertaining the object information from the recorded images with the aid of the machine learning model.

Method for ascertaining object information with the aid of image data

A robot device, a method for training a robot control model, and a method for controlling a robot device. The method for training includes: supplying an image showing object(s), to a first and second prediction model to produce a first and second pickup prediction that has, for each pixel of the image, a first and second pickup robot configuration vector with an assigned first and second success probability; supplying the first and second pickup prediction to a blending model of the robot control model to produce a third pickup prediction that has, for each pixel of the image: a third pickup robot configuration vector that is a weighted combination of the first and second pickup robot configuration vector, and a third success probability that is a weighted combination of the first and second success probability; and training the robot control model by adapting the first and second weighting factors.

Robot device, method for the computer-implemented training of a robot control model, and method for controlling a robot device

A method for predicting a future situation based on an analysis of at least one predictive pattern. The method comprises monitoring a plurality of events carried out by an event processing component, detecting a predictive pattern predictive of a future situation, selecting one of a plurality of proactive actions and an execution time according to its effect on at least one of a probability of occurrence and a cost of occurrence of the future situation, and outputting the selected proactive action.

Proactive event driven computing

Systems and methods for admission control to a physical host system are provided herein. One aspect provides for receiving at least one resource request at an admission control component of a distributed computing system, the at least one resource request comprised of at least one system type; processing the at least one resource request utilizing at least one physical host accessible to the distributed computing system; specifying a number of resource request slots to be reserved for at least one system type based on at least one future reservation threshold accessible to the admission control component; and blocking resource requests from entering the system through the admission control component based on a number of available resource request slots and the at least one future reservation threshold. Other embodiments and aspects are also described herein.

Host system admission control

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Haifa, Israel

Zohar Feldman