Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using simulated local demonstration data for robotic demonstration learning. One of the methods includes receiving perceptual data of a workcell of a robot to be configured to execute a task according to a skill template, wherein the skill template specifies one or more subtasks required to perform the skill, wherein at least one of the subtasks is a demonstration subtask that relies on learning visual characteristics of the workcell. A virtual model is generated of a portion of the workcell. A training system generates simulated local demonstration data from the virtual model of the portion of the workcell and tunes a base control policy for the demonstration subtask using the simulated local demonstration data generated from the virtual model of the portion of the workcell.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributing skill templates for robotic demonstration learning. One of the methods includes receiving from the user device by a skill template distribution system, a selection of an available skill template. The skill template distribution system provides a skill template, wherein the skill template comprises information representing a state machine of one or more tasks, and wherein the skill template specifies which of the one or more tasks are demonstration subtasks requiring local demonstration data. The skill template distribution system trains a machine learning model for the demonstration subtask using a local demonstration data to generate learned parameter values.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a demonstration device for robotic demonstration learning. One of the methods includes generating, by a demonstration device for a robot, a representation of a sequence of states input by a user of the demonstration device. The representation is provided by the demonstration device to a robot execution system. The representation of the sequence of actions is translated into a plurality of robot commands corresponding to the representation of the sequence of states input by the user on the demonstration device. The plurality of robot commands corresponding to the sequence of actions input by the user on the demonstration device are executed. Demonstration data is generated from one or more sensor streams of the robot while executing the plurality of robot commands corresponding to the sequence of actions input by the user on the demonstration device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributing skill templates for robotic demonstration learning. One of the methods includes receiving, from the user device by a skill template distribution system, a selection of an available skill template. The skill template distribution system provides a skill template, wherein the skill template comprises information representing a state machine of one or more tasks, and wherein the skill template specifies which of the one or more tasks are demonstration subtasks requiring local demonstration data. The skill template distribution system trains a machine learning model for the demonstration subtask using a local demonstration data to generate learned parameter values.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributed robotic demonstration learning. One of the methods includes receiving a skill template to be trained to cause a robot to perform a particular skill having a plurality of subtasks. One or more demonstration subtasks defined by the skill template are identified, wherein each demonstration subtask is an action to be refined using local demonstration data. On online execution system uploads sets of local demonstration data to a cloud-based training system. The cloud-based training system generates respective trained model parameters for each set of local demonstration data. The skill template is executed on the robot using the trained model parameters generated by the cloud-based training system.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a demonstration device for robotic demonstration learning. One of the methods includes generating, by a demonstration device for a robot, a representation of a sequence of states input by a user of the demonstration device. The representation is provided by the demonstration device to a robot execution system. The representation of the sequence of actions is translated into a plurality of robot commands corresponding to the representation of the sequence of states input by the user on the demonstration device. The plurality of robot commands corresponding to the sequence of actions input by the user on the demonstration device are executed. Demonstration data is generated from one or more sensor streams of the robot while executing the plurality of robot commands corresponding to the sequence of actions input by the user on the demonstration device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for integrating sensor streams for robotic demonstration learning. One of the methods includes selecting, by a learning system for a robot, a base update rate for combining multiple sensor streams into a task state representation. The learning system repeatedly generates the task state representation at the base update rate, including combining, during each time period defined by the update rate, the task state representation from most recently updated sensor data processed by the plurality of neural networks. The learning system repeatedly uses the task state representations to generate commands for the robot at the base update rate.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a demonstration device for robotic demonstration learning. One of the methods includes generating, by a demonstration device for a robot, a representation of a sequence of states input by a user of the demonstration device. The representation is provided by the demonstration device to a robot execution system. The representation of the sequence of actions is translated into a plurality of robot commands corresponding to the representation of the sequence of states input by the user on the demonstration device. The plurality of robot commands corresponding to the sequence of actions input by the user on the demonstration device are executed. Demonstration data is generated from one or more sensor streams of the robot while executing the plurality of robot commands corresponding to the sequence of actions input by the user on the demonstration device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributed robotic demonstration learning. One of the methods includes receiving a skill template to be trained to cause a robot to perform a particular skill having a plurality of subtasks. One or more demonstration subtasks defined by the skill template are identified, wherein each demonstration subtask is an action to be refined using local demonstration data. On online execution system uploads sets of local demonstration data to a cloud-based training system. The cloud-based training system generates respective trained model parameters for each set of local demonstration data. The skill template is executed on the robot using the trained model parameters generated by the cloud-based training system.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a demonstration device for robotic demonstration learning. One of the methods includes generating, by a demonstration device for a robot, a representation of a sequence of states input by a user of the demonstration device. The representation is provided by the demonstration device to a robot execution system. The representation of the sequence of actions is translated into a plurality of robot commands corresponding to the representation of the sequence of states input by the user on the demonstration device. The plurality of robot commands corresponding to the sequence of actions input by the user on the demonstration device are executed. Demonstration data is generated from one or more sensor streams of the robot while executing the plurality of robot commands corresponding to the sequence of actions input by the user on the demonstration device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for integrating sensor streams for robotic demonstration learning. One of the methods includes selecting, by a learning system for a robot, a base update rate for combining multiple sensor streams into a task state representation. The learning system repeatedly generates the task state representation at the base update rate, including combining, during each time period defined by the update rate, the task state representation from most recently updated sensor data processed by the plurality of neural networks. The learning system repeatedly uses the task state representations to generate commands for the robot at the base update rate.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using skill templates for robotic demonstration learning. One of the methods includes receiving a skill template for a task to be performed by a robot, wherein the skill template defines a state machine having a plurality of subtasks and one or more respective transition conditions between one or more of the subtasks. Local demonstration data for a demonstration subtask of the skill template is received, where the local demonstration data is generated from a user demonstrating how to perform the demonstration subtask with the robot. A machine learning model is refined for the demonstration subtask and the skill template is executed on the robot, causing the robot to transition through the state machine defined by the skill template to perform the task.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributing skill templates for robotic demonstration learning. One of the methods includes receiving, from the user device by a skill template distribution system, a selection of an available skill template. The skill template distribution system provides a skill template, wherein the skill template comprises information representing a state machine of one or more tasks, and wherein the skill template specifies which of the one or more tasks are demonstration subtasks requiring local demonstration data. The skill template distribution system trains a machine learning model for the demonstration subtask using a local demonstration data to generate learned parameter values.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using simulated local demonstration data for robotic demonstration learning. One of the methods includes receiving perceptual data of a workcell of a robot to be configured to execute a task according to a skill template, wherein the skill template specifies one or more subtasks required to perform the skill, wherein at least one of the subtasks is a demonstration subtask that relies on learning visual characteristics of the workcell. A virtual model is generated of a portion of the workcell. A training system generates simulated local demonstration data from the virtual model of the portion of the workcell and tunes a base control policy for the demonstration subtask using the simulated local demonstration data generated from the virtual model of the portion of the workcell.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing online robotic motion planning from pre-generated motion plans. A library of pre-generated motion plans for performing a particular task is maintained. Each pre-generated motion plan comprises a plurality of waypoints and one or more actions. One or more present observations of a robot in a workcell are obtained. The one or more observations are classified. A pre-generated candidate motion plan that matches the labels assigned to the present observations of the robot in the workcell is selected from the library of pre-generated motion plans. The pre-generated candidate motion plan is adapted according to the present observations of the robot in the workcell to generate a final motion plan to be executed by the robot.

Abstract: Pharmaceutical formulation in the form of agglomerates comprising A) an excipient content composed of a) 60-97% by weight of sugar or sugar alcohols, b) 1-25% by weight of a disintegrant, c) 1-15% by weight of water-insoluble, film-forming polymers d) 0-15% by weight of water-soluble polymers and e) 0-15% by weight of further pharmaceutically customary excipients the total of the components a) to e) being 100% by weight, and B) at least one active ingredient.

Abstract: Described is a storage-stable dust-free homogeneous particulate formulation. The formulation consists of (a) at least one water-soluble Vitamin E-derivative, (b) at least one hydrophilic polymer, (c) optionally additional surface-active substances, and (d) optionally additional pharmaceutical additives. The sum of (a), (b), (c) and (d) equals 100% by weight of the formulation. The fines fraction with particle diameters of less than 100 ?m is less than 10% by weight. Describe also is a process for manufacturing the formulation, and use of the formulation as a solubilizing composition in pharmaceutical formulations.

Abstract: A storage-stable dust-free homogeneous particulate formulation comprising at least one water-soluble Vitamin E-derivative and at least one hydrophilic polymer. In one embodiment the storage-stable dust-free homogeneous particle formulation, consists of (a) at least one water-soluble Vitamin E-derivative, (b) at least one hydrophilic polymer, (c) optionally additional surface-active substances, and (d) optionally additional pharmaceutical additives, with the proviso, that the sum of (a), (b), (c) and (d) equals 100% by weight of the formulation, and wherein the fines fraction with particle diameters of less than 100 ?m is less than 10% by weight. Methods of making the particulate formulation by a spray granulation process are also provided.

Abstract: The present invention relates to a method for the production of solid solutions of sparingly soluble pesticides, pulverulent products that are obtained by said method and their use for pesticide formulations.

Abstract: The present invention comprises a process for purifying isocyanate-comprising residues.