Abstract: A robot control system, comprising: a first robot; a second robot; and circuitry configured to: control the first robot so that the first robot supports a first part of an indefinite-shape object placed at a predetermined location; and control the second robot so that the second robot supports a second part of the indefinite-shape object to convey the indefinite-shape object in cooperation with the first robot, the second part being different from the first part.

Abstract: A robot control system comprising circuitry configured to acquire a designated area image showing a designated area, and execute image analysis on the designated area image to detect a current state of the designated area as a current area state. The circuitry is further configured to generate, based on the current area state, a state of the designated area which simulates one or more objects including an additional object as having been placed in the designated area, as a predicted area state. The circuitry is further configured to generate object information on the additional object which is simulated in the designated area, based on the predicted area state. The circuitry is further configured to control a robot so as to physically place the additional object in the designated area in accordance with the object information.

Abstract: A robot control system, comprising: a robot configured to hold a cutter; and circuitry configured to control the robot so that the robot cuts a packaging material with a blade of the cutter, and control the robot so that the blade is oriented more toward an outer side of the packaging material when the robot cuts a face of the packaging material with the blade than when the robot cuts a corner of the packaging material with the blade.

Abstract: A material separation system includes circuitry configured to acquire a composite image capturing a plurality of commingled pieces of material, and sets a number of candidate separation positions based on the composite image. The circuitry is further configured to identify, for each of the candidate separation positions, a subset of the plurality of commingled pieces of material, and calculate an estimated mass of each subset of the plurality of commingled pieces of material. The circuitry is further configured to determine a position for separating a predetermined mass of pieces of material from the plurality of commingled pieces of material, as a separation position, based on a comparison of the predetermined mass with the estimated mass corresponding to each of the candidate separation positions, and separate the predetermined mass of pieces of material from the plurality of commingled pieces of material at the separation position.

Abstract: Provided a machine learning data generation device including: at least one processor; and at least one memory device that stores a plurality of instructions which, when executed by the at least one processor, causes the at least one processor to execute: acquiring , in association with a predetermined label actual time series information; executing physical simulation of generating a plurality of pieces of virtual time series information; identifying parameter values based on the plurality of pieces of virtual time series information and the actual time series information, and to associate the identified parameter values with the label; generating a new parameter value and the label based on the identified parameter values; generating virtual time series information corresponding to a new internal state by executing physical simulation through use of the new parameter value; and generating new machine learning data.

Abstract: Provided is a machine learning data generation device including: a virtual sensor input generator configured to generate a virtual sensor input, which is obtained by virtually generating a sensor input obtained as a result of performing sensing, by a sensor of a work machine, on a plurality of randomly piled subjects to be subjected to physical work by an operating machine of the work machine; a virtual operation command generator configured to generate a virtual operation command, which is obtained by virtually generating an operation command for the operating machine of the work machine; a virtual operation outcome evaluator configured to evaluate an outcome of the physical work in response to the virtual operation command in a virtual space; and a machine learning data generator configured to generate machine learning data based on the virtual sensor input, the virtual operation command, and the evaluation of the virtual operation outcome evaluator.