Abstract: Apparatuses, methods, and systems for generating gridded predictions of a probability of wildfire spread for geographies are disclosed. One method includes sensing observational climate and earth surface data, obtaining historical data on wildfire spread events, obtaining gridded climate data, creating a set of input features, creating a gridded wildfire data set, training a model that learns one or more probabilistic mapping function emulators between the set of input features and the gridded wildfire data set, which predicts a first probability of wildfire occurrence and a rate and extent of wildfire spread within a geographical region and at a specified period of time, and generating gridded wildfire prediction data including a second probability of wildfire occurrence and spread within the geographical region, using the model and a new set of input features over the geographical region but for a different time period.

Abstract: An example operation includes one or more of receiving an opt-in request to display information on a display of a vehicle configured to be viewed from outside of the vehicle, authenticating the request to allow displaying information on the display, determining information associated with the vehicle to be displayed on the display, determining misuse of the information displayed on the display based on a notification from another vehicle, and flagging the misuse.

Abstract: Wearable spectroscopy systems and methods for identifying one or more characteristics of a target object are described. Spectroscopy systems may include a light source configured to emit light in an irradiated field of view and an electromagnetic radiation detector configured to receive reflected light from a target object irradiated by the light source. One or more processors of the systems may identify a characteristic of the target object based on a determined level of light absorption by the target object. Some systems and methods may include one or more corrections for scattered and/or ambient light such as applying an ambient light correction, passing the reflected light through an anti-scatter grid, or using a time-dependent variation in the emitted light.

Abstract: Methods, apparatus, and computer readable media related to combining and/or training one or more neural network modules based on version identifier(s) assigned to the neural network module(s). Some implementations are directed to using version identifiers of neural network modules in determining whether and/or how to combine multiple neural network modules to generate a combined neural network model for use by a robot and/or other apparatus. Some implementations are additionally or alternatively directed to assigning a version identifier to an endpoint of a neural network module based on one or more other neural network modules to which the neural network module is joined during training of the neural network module.

Abstract: Utilizing at least one existing policy (e.g. a manually engineered policy) for a robotic task, in generating reinforcement learning (RL) data that can be used in training an RL policy for an instance of RL of the robotic task. The existing policy can be one that, standing alone, will not generate data that is compatible with the instance of RL for the robotic task. In contrast, the generated RL data is compatible with RL for the robotic task at least by virtue of it including state data that is in a state space of the RL for the robotic task, and including actions that are in the action space of the RL for the robotic task. The generated RL data can be used in at least some of the initial training for the RL policy using reinforcement learning.

Abstract: Techniques are described herein for robotic control using value distributions. In various implementations, as part of performing a robotic task, state data associated with the robot in an environment may be generated based at least in part on vision data captured by a vision component of the robot. A plurality of candidate actions may be sampled, e.g., from continuous action space. A trained critic neural network model that represents a learned value function may be used to process a plurality of state-action pairs to generate a corresponding plurality of value distributions. Each state-action pair may include the state data and one of the plurality of sampled candidate actions. The state-action pair corresponding to the value distribution that satisfies one or more criteria may be selected from the plurality of state-action pairs. The robot may then be controlled to implement the sampled candidate action of the selected state-action pair.

Abstract: Methods and apparatus related to receiving a request that includes robot instructions and/or environmental parameters, operating each of a plurality of robots based on the robot instructions and/or in an environment configured based on the environmental parameters, and storing data generated by the robots during the operating. In some implementations, at least part of the stored data that is generated by the robots is provided in response to the request and/or additional data that is generated based on the stored data is provided in response to the request.

Abstract: Implementations disclosed herein relate to utilizing at least one existing manually engineered policy, for a robotic task, in training an RL policy model that can be used to at least selectively replace a portion of the engineered policy. The RL policy model can be trained for replacing a portion of a robotic task and can be trained based on data from episodes of attempting performance of the robotic task, including episodes in which the portion is performed based on the engineered policy and/or other portion(s) are performed based on the engineered policy. Once trained, the RL policy model can be used, at least selectively and in lieu of utilization of the engineered policy, to perform the portion of robotic task, while other portion(s) of the robotic task are performed utilizing the engineered policy and/or other similarly trained (but distinct) RL policy model(s).

Abstract: Deep machine learning methods and apparatus related to the manipulation of an object by an end effector of a robot are described herein. Some implementations relate to training an action prediction network to predict a probability density which can include candidate actions of successful grasps by the end effector given an input image. Some implementations are directed to utilization of an action prediction network to visually servo a grasping end effector of a robot to achieve a successful grasp of an object by the grasping end effector.

Abstract: Methods and apparatus related to receiving a request that includes robot instructions and/or environmental parameters, operating each of a plurality of robots based on the robot instructions and/or in an environment configured based on the environmental parameters, and storing data generated by the robots during the operating. In some implementations, at least part of the stored data that is generated by the robots is provided in response to the request and/or additional data that is generated based on the stored data is provided in response to the request.

Abstract: Methods, apparatus, systems, and computer-readable media are provided for generating spatial affordances for an object, in an environment of a robot, and utilizing the generated spatial affordances in one or more robotics applications directed to the object. Various implementations relate to applying vision data as input to a trained machine learning model, processing the vision data using the trained machine learning model to generate output defining one or more spatial affordances for an object captured by the vision data, and controlling one or more actuators of a robot based on the generated output. Various implementations additionally or alternatively relate to training such a machine learning model.

Abstract: Methods, apparatus, and computer readable media related to combining and/or training one or more neural network modules based on version identifier(s) assigned to the neural network module(s). Some implementations are directed to using version identifiers of neural network modules in determining whether and/or how to combine multiple neural network modules to generate a combined neural network model for use by a robot and/or other apparatus. Some implementations are additionally or alternatively directed to assigning a version identifier to an endpoint of a neural network module based on one or more other neural network modules to which the neural network module is joined during training of the neural network module.

Abstract: Deep machine learning methods and apparatus related to the manipulation of an object by an end effector of a robot are described herein. Some implementations relate to training an action prediction network to predict a probability density which can include candidate actions of successful grasps by the end effector given an input image. Some implementations are directed to utilization of an action prediction network to visually servo a grasping end effector of a robot to achieve a successful grasp of an object by the grasping end effector.

Abstract: Methods and apparatus related to receiving a request that includes robot instructions and/or environmental parameters, operating each of a plurality of robots based on the robot instructions and/or in an environment configured based on the environmental parameters, and storing data generated by the robots during the operating. In some implementations, at least part of the stored data that is generated by the robots is provided in response to the request and/or additional data that is generated based on the stored data is provided in response to the request.

Abstract: Methods, apparatus, systems, and computer-readable media are provided for generating spatial affordances for an object, in an environment of a robot, and utilizing the generated spatial affordances in one or more robotics applications directed to the object. Various implementations relate to applying vision data as input to a trained machine learning model, processing the vision data using the trained machine learning model to generate output defining one or more spatial affordances for an object captured by the vision data, and controlling one or more actuators of a robot based on the generated output. Various implementations additionally or alternatively relate to training such a machine learning model.

Abstract: Methods and apparatus related to receiving a request that includes robot instructions and/or environmental parameters, operating each of a plurality of robots based on the robot instructions and/or in an environment configured based on the environmental parameters, and storing data generated by the robots during the operating. In some implementations, at least part of the stored data that is generated by the robots is provided in response to the request and/or additional data that is generated based on the stored data is provided in response to the request.

Abstract: The present invention relates to a tethering device for snowboard, comprising: a housing; a tether adapted to be accommodated in the housing, the tether having a first end connected to the housing, and a second end extendable away from the housing; an engaging means arranged at the second end of the tether, the engaging means having a receiving portion receivable at a guide portion of the housing; wherein the receiving portion is profiled to conform with the guide portion such that, the receiving portion, when arranged to be received at the guide portion, is positioned in a predefined orientation.

Abstract: The present invention relates to a tethering device for snowboard, comprising: a housing; a tether adapted to be accommodated in the housing, the tether having a first end connected to the housing, and a second end extendable away from the housing; an engaging means arranged at the second end of the tether, the engaging means having a receiving portion receivable at a guide portion of the housing; wherein the receiving portion is profiled to conform with the guide portion such that, the receiving portion, when arranged to be received at the guide portion, is positioned in a predefined orientation.