Abstract: A domain specific language, or Scenario Description Language (SDL), can be used for quickly enumerating scenarios in a simulation for testing and validating interaction of an object (e.g., an autonomous vehicle) within an environment. Scenarios in a simulation are defined using one or more primitives. Primitives are used to define objects to be instantiated (such as body size, position, orientation, velocities, etc.) and/or actions to be performed by the objects in the simulation (such as wait for a period of time, goal positions, follow a particular object, etc.). The SDL enables simple creation of multiple scenarios by combining primitives combinatorially and in some examples, limiting which scenarios are created to those that correspond to combinations that provide meaningful information. Additionally, the SDL allows for instantiation to be agnostic of map features so that a particular scenario can be instantiated automatically over all possible positions within a map.

Abstract: A domain specific language, or Scenario Description Language (SDL), can be used for quickly enumerating scenarios in a simulation for testing and validating interaction of an object (e.g., an autonomous vehicle) within an environment. Scenarios in a simulation are defined using one or more primitives. Primitives are used to define objects to be instantiated (such as body size, position, orientation, velocities, etc.) and/or actions to be performed by the objects in the simulation (such as wait for a period of time, goal positions, follow a particular object, etc.). The SDL enables simple creation of multiple scenarios by combining primitives combinatorially and in some examples, limiting which scenarios are created to those that correspond to combinations that provide meaningful information. Additionally, the SDL allows for instantiation to be agnostic of map features so that a particular scenario can be instantiated automatically over all possible positions within a map.

Abstract: A domain specific language, or Scenario Description Language (SDL), can be used for quickly enumerating scenarios in a simulation for testing and validating interaction of an object (e.g., an autonomous vehicle) within an environment. Scenarios in a simulation are defined using one or more primitives. Primitives are used to define objects to be instantiated (such as body size, position, orientation, velocities, etc.) and/or actions to be performed by the objects in the simulation (such as wait for a period of time, goal positions, follow a particular object, etc.). The SDL enables simple creation of multiple scenarios by combining primitives combinatorially and in some examples, limiting which scenarios are created to those that correspond to combinations that provide meaningful information. Additionally, the SDL allows for instantiation to be agnostic of map features so that a particular scenario can be instantiated automatically over all possible positions within a map.

Abstract: Example methods and systems for determining when to launch vehicles into a fleet of autonomous vehicles are described. A method comprises receiving a sequence of coverage requirements for a region over a period of time. The region may be characterized by landmarks and the period of time can be divided into time intervals. The method also includes defining a landmark as a launch site representative of a landmark at which a given vehicle can be added to a plurality of operating vehicles, and determining for a respective landmark, estimated landmarks that can be reached by a vehicle starting from the respective landmark by an end of a time interval. The method also includes based on the sequence of coverage requirements and the estimated landmarks, determining a given launch site and corresponding time interval at which to add the given vehicle to the plurality of operating vehicles.

Abstract: A computer-implemented method includes accessing a plurality of sets of outputs for an interactive animation, with each set of outputs being associated with a different sequence of a plurality of sequences of discrete control inputs, and with each set of outputs comprising an output that provides a stored portion of the animation; and transmitting, to a client device, information indicative of at least one of the plurality of sets of outputs for the animation and the output that provides the stored portion of the animation, which when rendered by the client device causes the animation to be presented to a user.

Abstract: A domain specific language, or Scenario Description Language (SDL), can be used for quickly enumerating scenarios in a simulation for testing and validating interaction of an object (e.g., an autonomous vehicle) within an environment. Scenarios in a simulation are defined using one or more primitives. Primitives are used to define objects to be instantiated (such as body size, position, orientation, velocities, etc.) and/or actions to be performed by the objects in the simulation (such as wait for a period of time, goal positions, follow a particular object, etc.). The SDL enables simple creation of multiple scenarios by combining primitives combinatorially and in some examples, limiting which scenarios are created to those that correspond to combinations that provide meaningful information. Additionally, the SDL allows for instantiation to be agnostic of map features so that a particular scenario can be instantiated automatically over all possible positions within a map.

Abstract: Example systems and methods may facilitate processing of voice commands using a hybrid system with automated processing and human guide assistance. An example method includes receiving a speech segment, determining a textual representation of the speech segment, causing one or more guide computing devices to display one or more portions of the textual representation, receiving input data from the one or more guide computing devices that identifies a plurality of chunks of the textual representation, determining an association between the identified chunks of the textual representation and corresponding semantic labels, and determining a digital representation of a task based on the identified chunks of the textual representation and the corresponding semantic labels.

Abstract: Techniques are disclosed for providing a learning-based clothing model that enables the simultaneous animation of multiple detailed garments in real-time. A simple conditional model learns and preserves key dynamic properties of cloth motions and folding details. Such a conditional model may be generated for each garment worn by a given character. Once generated, the conditional model may be used to determine complex body/cloth interactions in order to render the character and garment from frame-to-frame. The clothing model may be used for a variety of garments worn by male and female human characters (as well as non-human characters) while performing a varied set of motions typically used in video games (e.g., walking, running, jumping, turning, etc.).

Abstract: A computer-implemented method includes accessing a plurality of sets of outputs for an interactive animation, with each set of outputs being associated with a different sequence of a plurality of sequences of discrete control inputs, and with each set of outputs comprising an output that provides a stored portion of the animation; and transmitting, to a client device, information indicative of at least one of the plurality of sets of outputs for the animation and the output that provides the stored portion of the animation, which when rendered by the client device causes the animation to be presented to a user.

Abstract: Methods and systems for determining trajectories for vehicles of a fleet of vehicles are provided. In one example, a method comprises receiving an initial location of one or more vehicles, and receiving a sequence of coverage requirements for a region and an associated period of time. The region may be divided into a plurality of landmarks and the period of time may be divided into a plurality of phases. The method also comprises determining for each of one or more phases and at least one respective landmark, a set of starting landmarks from which a vehicle could reach the respective landmark during the phase. The method further comprises determining which respective landmark that the vehicle should travel to during the one or more phases based on the sequence of coverage requirements and the set of starting landmarks for the one or more phases and the at least one respective landmark.

Abstract: Example methods and systems for determining when to launch vehicles into a fleet of autonomous vehicles are described. A method comprises receiving a sequence of coverage requirements for a region over a period of time. The region may be characterized by landmarks and the period of time can be divided into time intervals. The method also includes defining a landmark as a launch site representative of a landmark at which a given vehicle can be added to a plurality of operating vehicles, and determining for a respective landmark, estimated landmarks that can be reached by a vehicle starting from the respective landmark by an end of a time interval. The method also includes based on the sequence of coverage requirements and the estimated landmarks, determining a given launch site and corresponding time interval at which to add the given vehicle to the plurality of operating vehicles.

Abstract: Methods and systems for determining trajectories for vehicles of a fleet of vehicles are provided. In one example, a method comprises receiving an initial location of one or more vehicles, and receiving a sequence of coverage requirements for a region and an associated period of time. The region may be divided into a plurality of landmarks and the period of time may be divided into a plurality of phases. The method also comprises determining for each of one or more phases and at least one respective landmark, a set of starting landmarks from which a vehicle could reach the respective landmark during the phase. The method further comprises determining which respective landmark that the vehicle should travel to during the one or more phases based on the sequence of coverage requirements and the set of starting landmarks for the one or more phases and the at least one respective landmark.

Abstract: Methods and systems for determining trajectories for vehicles of a fleet of vehicles are provided. In one example, a method comprises receiving an initial location of one or more vehicles, and receiving a sequence of coverage requirements for a region and an associated period of time. The region may be divided into a plurality of landmarks and the period of time may be divided into a plurality of phases. The method also comprises determining for each of one or more phases and at least one respective landmark, a set of starting landmarks from which a vehicle could reach the respective landmark during the phase. The method further comprises determining which respective landmark that the vehicle should travel to during the one or more phases based on the sequence of coverage requirements and the set of starting landmarks for the one or more phases and the at least one respective landmark.

Abstract: Methods and systems for determining a cyclical pattern of trajectories for a fleet of vehicles are provided. In one example, a method comprises receiving a sequence of coverage requirements for a region and an associated period of time. For each of one or more phases of the period of time, possible routes that a vehicle located at one or more respective landmarks at a beginning of the phase could follow to reach one or more additional landmarks by an end of the phase are determined. Further, a cyclical pattern of trajectories for vehicles of a fleet of vehicles that minimizes a difference between a distribution of the fleet at a beginning of the period of time and a distribution of the fleet at an end of the period of time is determined.

Abstract: Methods and systems for determining trajectories for vehicles of a fleet of vehicles are provided. In one example, a method comprises receiving an initial location of one or more vehicles, and receiving a sequence of coverage requirements for a region and an associated period of time. The region may be divided into a plurality of landmarks and the period of time may be divided into a plurality of phases. The method also comprises determining for each of one or more phases and at least one respective landmark, a set of starting landmarks from which a vehicle could reach the respective landmark during the phase. The method further comprises determining which respective landmark that the vehicle should travel to during the one or more phases based on the sequence of coverage requirements and the set of starting landmarks for the one or more phases and the at least one respective landmark.

Abstract: Techniques are disclosed for providing a learning-based clothing model that enables the simultaneous animation of multiple detailed garments in real-time. A simple conditional model learns and preserves key dynamic properties of cloth motions and folding details. Such a conditional model may be generated for each garment worn by a given character. Once generated, the conditional model may be used to determine complex body/cloth interactions in order to render the character and garment from frame-to-frame. The clothing model may be used for a variety of garments worn by male and female human characters (as well as non-human characters) while performing a varied set of motions typically used in video games (e.g., walking, running, jumping, turning, etc.).