Abstract: Techniques for determine tax information for an entity based in part on plotting an entity address into one of a set of polygons respectively associated with taxing jurisdictions are disclosed. Initially, the system partitions a geographical region into a plurality of polygons based on geospatial files. The polygons correspond to taxing jurisdictions. The system determines geographical coordinates for an address, e.g. residence of an entity, employee, and the geographical coordinates are used to plot the first address within the geographical region. The system identifies the polygons that include the address and determines the tax jurisdictions corresponding to the polygons. Based on the tax jurisdictions and tax attributes of the entity, the system calculates a set of tax information for the entity that are presented to a user for viewing.

Abstract: One embodiment of a method for controlling a robot includes performing a plurality of simulations of a robot interacting with one or more objects represented by one or more signed distance functions (SDFs), where performing the plurality of simulations comprises reducing a number of contacts between the one or more objects that are being simulated, and updating one or more parameters of a machine learning model based on the plurality of simulations to generate a trained machine learning model.

Abstract: In various examples, systems and methods are disclosed relating to dexterous arm-hand grasping with geometric fabrics. One or more processors can update, during a first update stage, a teacher model to generate first actions for a geometric fabric associated with a simulated autonomous machine of a simulation using state information of the simulation. During a second update stage, the processors can update a student model to generate second actions for the geometric fabric using at least one rendered image of the simulation, the teacher model, and noised state information of the simulation. The student model and the geometric fabric can be used to control a physical autonomous machine with respect to a physical object based at least on an image of an environment including the physical autonomous machine and the physical object.

Abstract: In various examples, systems and methods are disclosed relating to disclosed relating to dexterous arm-hand grasping with geometric fabrics. One or more processors can cause a teacher model to generate first actions for a geometric fabric associated with a simulated autonomous machine in a simulated environment using state information of the simulated environment and position information of a simulated object in the simulated environment. Using the teacher model and a depth image of the simulated environment, a student model can be updated to generate second actions for the geometric fabric associated with the simulated autonomous machine. A depth image of an environment can be provided as input to the student model to cause the student model to infer at least one action to control a physical autonomous machine with respect to a physical object using the geometric fabric.

Abstract: A machine-learning control system is trained to perform a task using a simulation. The simulation is governed by parameters that, in various embodiments, are not precisely known. In an embodiment, the parameters are specified with an initial value and expected range. After training on the simulation, the machine-learning control system attempts to perform the task in the real world. In an embodiment, the results of the attempt are compared to the expected results of the simulation, and the parameters that govern the simulation are adjusted so that the simulated result matches the real-world attempt. In an embodiment, the machine-learning control system is retrained on the updated simulation. In an embodiment, as additional real-world attempts are made, the simulation parameters are refined and the control system is retrained until the simulation is accurate and the control system is able to successfully perform the task in the real world.

Abstract: Apparatuses, systems, and techniques to perform collision-free motion generation (e.g., to operate a real-world or virtual robot). In at least one embodiment, at least a portion of the collision-free motion generation is performed in parallel.

Abstract: One embodiment of a method for controlling a robot includes receiving sensor data indicating a state of the robot, generating an action based on the sensor data and a trained machine learning model, computing a target state of the robot based on the action and a previous target state of the robot, and causing the robot to move based on the target state of the robot.

Abstract: Apparatuses, systems, and techniques are disclosed for controlling a robot to execute a task. In at least one embodiment, a current image of the robot in an environment and a text describing the task are obtained. A future image of the robot in the environment is predicted based on the current image and the text. Subsequently, one or more actions are predicted based on the current image, the future image, and the text. The one or more actions can move the robot from a first state corresponding to the current image to a second state corresponding to the future image. The robot executes the sequence of actions to move in the environment.

Abstract: A computer-implemented technique for training machine learning models includes processing one or more input images using a trained image generative model to generate one or more augmented images, where the trained image generative model generates each augmented image included in the one or more augmented images conditioned on an input image included in the one or more input images, depth information associated with the input image, semantic information associated with the input image, and text describing an augmentation to make to the input image; and performing, based on the one or more augmented images, one or more operations to train an untrained machine learning model to generate a trained machine learning model.

Abstract: The disclosed method for training a robot control model includes generating, using one or more simulations, a plurality of disassembly trajectories along which a first part is disassembled from a second part; reversing the plurality of disassembly trajectories to generate a plurality of reversed disassembly trajectories; and performing, based on the plurality of reversed disassembly trajectories, one or more operations to train an untrained machine learning model to generate a trained machine learning model, wherein the trained machine learning model is trained to control a robot to assemble the first part and the second part.

Abstract: The disclosed method for training a machine learning model to control a robot includes performing, based on demonstration data associated with one or more assembly tasks, one or more first training operations to generate one or more first trained machine learning models, wherein each first trained machine learning included in the one or more first trained machine learning models is trained to control a robot to perform a different assembly task, and performing, based on the one or more first trained machine learning models and one or more geometries associated with one or more parts, one or more second training operations to generate a second trained machine learning model, wherein the second trained machine learning model is trained to control the robot to perform a plurality of assembly tasks.

Abstract: Apparatuses, systems, and techniques to perform collision-free motion generation (e.g., to operate a real-world or virtual robot). In at least one embodiment, at least a portion of the collision-free motion generation is performed in parallel.

Abstract: Apparatuses, systems, and techniques to generate a predicted outcome of an object resulting from a robotic component applying a force. In at least one embodiment, a predicted outcome of an object resulting from a robotic component applying a force is generated based on, for example, a neural network.

Abstract: A technique for training a neural network, including generating a plurality of input vectors based on a first plurality of task demonstrations associated with a first robot performing a first task in a simulated environment, wherein each input vector included in the plurality of input vectors specifies a sequence of poses of an end-effector of the first robot, and training the neural network to generate a plurality of output vectors based on the plurality of input vectors. Another technique for generating a task demonstration, including generating a simulated environment that includes a robot and at least one object, causing the robot to at least partially perform a task associated with the at least one object within the simulated environment based on a first output vector generated by a trained neural network, and recording demonstration data of the robot at least partially performing the task within the simulated environment.

Abstract: A human pilot controls a robotic arm and gripper by simulating a set of desired motions with the human hand. In at least one embodiment, one or more images of the pilot's hand are captured and analyzed to determine a set of hand poses. In at least one embodiment, the set of hand poses is translated to a corresponding set of robotic-gripper poses. In at least one embodiment, a set of motions is determined that perform the set of robotic-gripper poses, and the robot is directed to perform the set of motions.

Abstract: Apparatuses, systems, and techniques to cause actions to be performed by an autonomous machine in a previously unknown environment. In at least one embodiment, one or more neural networks are trained based, at least in part, on images of one or more automatically generated training actions.

Abstract: A machine-learning control system is trained to perform a task using a simulation. The simulation is governed by parameters that, in various embodiments, are not precisely known. In an embodiment, the parameters are specified with an initial value and expected range. After training on the simulation, the machine-learning control system attempts to perform the task in the real world. In an embodiment, the results of the attempt are compared to the expected results of the simulation, and the parameters that govern the simulation are adjusted so that the simulated result matches the real-world attempt. In an embodiment, the machine-learning control system is retrained on the updated simulation. In an embodiment, as additional real-world attempts are made, the simulation parameters are refined and the control system is retrained until the simulation is accurate and the control system is able to successfully perform the task in the real world.

Abstract: In various examples, systems and methods are disclosed relating to geometric fabrics for accelerated policy learning and sim-to-real transfer in robotics systems, platforms, and/or applications. For example, a system can provide an input indicative of a goal pose for a robot to a model to cause the model to generate an output, the output representing a plurality of points along a path for movement of the robot to the goal pose; and generate one or more control signals for operation of the robot based at least on the plurality of points along the path and a policy corresponding to one or more criteria for the operation of the robot. In examples, the system can provide the one or more control signals to the robot to cause the robot to move toward the goal pose.

Abstract: A technique for training a neural network, including generating a plurality of input vectors based on a first plurality of task demonstrations associated with a first robot performing a first task in a simulated environment, wherein each input vector included in the plurality of input vectors specifies a sequence of poses of an end-effector of the first robot, and training the neural network to generate a plurality of output vectors based on the plurality of input vectors. Another technique for generating a task demonstration, including generating a simulated environment that includes a robot and at least one object, causing the robot to at least partially perform a task associated with the at least one object within the simulated environment based on a first output vector generated by a trained neural network, and recording demonstration data of the robot at least partially performing the task within the simulated environment.

Abstract: One embodiment of a method for controlling a robot includes receiving sensor data indicating a state of the robot, generating an action based on the sensor data and a trained machine learning model, computing a target state of the robot based on the action and a previous target state of the robot, and causing the robot to move based on the target state of the robot.