Abstract: Apparatuses, systems, and techniques to update a machine learning model associated with an object. In at least one embodiment, the machine learning model is updated based at least in part on, for example, one or more distributions associated with the machine learning model.

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: Candidate grasping models of a deformable object are applied to generate a simulation of a response of the deformable object to the grasping model. From the simulation, grasp performance metrics for stress, deformation controllability, and instability of the response to the grasping model are obtained, and the grasp performance metrics are correlated with robotic grasp features.

Abstract: A differentiable simulator for simulating the cutting of soft materials by a cutting instrument is provided. In accordance with one aspect of the disclosure, a method for simulating a cutting operation includes: receiving a mesh for an object, modifying the mesh to add virtual nodes associated with a predefined cutting plane, optimizing a set of parameters associated with a simulator based on ground-truth data, and running a simulation via the simulator to generate outputs that include trajectories associated with a cutting instrument. Optimizing the set of parameters can include performing inference based on a set of ground-truth trajectories captured using sensors to measure real-world cutting operations. The inference techniques can employ stochastic gradient descent, stochastic gradient Langevin dynamics, or a Bayesian approach. In an embodiment, the simulator can be utilized to generate control signals for a robot based on the simulated trajectories.

Abstract: A cable driving a large system such as cable driven machines, cable cars or tendons in a human or robot is typically modeled as a large number of small segments that are connected via joints. The two main difficulties with this model are satisfying the inextensibility constraint and handling the typically large mass ratio between the segments and the objects they connect. This disclosure introduces an effective approach to solving these problems. The introduced approach simulates the effect of a cable using a new type of distance constraint called ‘cable joint’ that changes both its attachment points and its rest length dynamically. The introduced approach models a cable connecting a series of objects, e.g., components of a robot, as a sequence of cable joints, reducing the complexity of the simulation from the order of the number of segments in the cable to the number of connected objects.

Abstract: Apparatuses, systems, and techniques to model a tactile force sensor. In at least one embodiment, output of tactile sensor is predicted from a modeled force and shape imposed on the sensor. In at least one embodiment, a shape of the surface of the tactile sensor is determined based at least in part on electrical signals received from the sensor.

Abstract: Candidate grasping models of a deformable object are applied to generate a simulation of a response of the deformable object to the grasping model. From the simulation, grasp performance metrics for stress, deformation controllability, and instability of the response to the grasping model are obtained, and the grasp performance metrics are correlated with robotic grasp features.

Abstract: Apparatuses, systems, and techniques apply to a force-based (e.g., primal) formulation for object simulation. In at least one embodiment, updates to the force-based formulation is determined by solving for constraints that are to be satisfied when simulating rigid bodies (e.g., contact rich scenarios).

Abstract: Embodiments of the present invention provide a position-based dynamics approach for simulating objects using a set of points and constraints, applied as equations that restrict the relative motion of bodies. Forces are applied to the points to move them, and the constraints ensure that the points will not move in a way that is inconsistent with rules of the simulation. The present invention improves upon existing PBD approaches by using regularized constraints that directly correspond to well-defined energy potentials, and which can advantageously be solved independent of time step and iteration count.

Abstract: Convolutional operators for triangle meshes are determined to construct one or more neural networks. In at least one embodiment, convolutional operators, pooling operators, and unpooling operators are determined to construct the one or more neural networks, in which the same learned weights from the one or more neural networks can further be used for triangle meshes with different topologies.

Abstract: A cable driving a large system such as cable driven machines, cable cars or tendons in a human or robot is typically modeled as a large number of small segments that are connected via joints. The two main difficulties with this model are satisfying the inextensibility constraint and handling the typically large mass ratio between the segments and the objects they connect. This disclosure introduces an effective approach to solving these problems. The introduced approach simulates the effect of a cable using a new type of distance constraint called ‘cable joint’ that changes both its attachment points and its rest length dynamically. The introduced approach models a cable connecting a series of objects, e.g., components of a robot, as a sequence of cable joints, reducing the complexity of the simulation from the order of the number of segments in the cable to the number of connected objects.

Abstract: This disclosure presents a process to generate one or more video frames through guiding the movements of a target object in an environment controlled by physics-based constraints. The target object is guided by the movements of a reference object from a motion capture (MOCAP) video clip. As disturbances, environmental factors, or other physics-based constraints interfere with the target object mimicking the reference object. A tracking agent, along with a corresponding neural network, can be used to compensate and modify the movements of the target object. Should the target object diverge significantly from the reference object, such as falling down, a recovery agent, along with a corresponding neural network, can be used to move the target object back into an approximate alignment with the reference object before resuming the tracking process.

Abstract: Embodiments of the present invention provide a novel method and discretization for animating water waves. The approaches disclosed combine the flexibility of a numerical approach to wave simulation with the stability and visual detail provided by a spectrum-based approach to provide Eulerian methods for simulating large-scale oceans with highly detailed wave features. A graphics processing unit stores a one-dimensional texture referred to as a wave profile buffer that stores pre-computed results at a number of discrete sample points for performing wave height evaluation. The water surface is rendered according to water height values computed using the wave profile, accounting for advection, spatial diffusion, angular diffusion, boundary reflections, and dissipation.

Abstract: A cable driving a large system such as cable driven machines, cable cars or tendons in a human or robot is typically modeled as a large number of small segments that are connected via joints. The two main difficulties with this approach are satisfying the inextensibility constraint and handling the typically large mass ratio between the small segments and the larger objects they connect. This disclosure introduces a more effective approach to solving these problems. The introduced approach simulates the effect of a cable instead of the cable itself using a new type of distance constraint called ‘cable joint’ that changes both its attachment points and its rest length dynamically. The introduced approach models a cable connecting a series of objects as a sequence of cable joints, reducing the complexity of the simulation from the order of the number of segments in the cable to the number of connected objects.

Abstract: Embodiments of the present invention provide a novel method and discretization for animating water waves. The approaches disclosed combine the flexibility of a numerical approach to wave simulation with the stability and visual detail provided by a spectrum-based approach to provide Eulerian methods for simulating large-scale oceans with highly detailed wave features. A graphics processing unit stores a one-dimensional texture referred to as a wave profile buffer that stores pre-computed results at a number of discrete sample points for performing wave height evaluation. The water surface is rendered according to water height values computed using the wave profile, accounting for advection, spatial diffusion, angular diffusion, boundary reflections, and dissipation.

Abstract: This disclosure presents a process to generate one or more video frames through guiding the movements of a target object in an environment controlled by physics-based constraints. The target object is guided by the movements of a reference object from a motion capture (MOCAP) video clip. As disturbances, environmental factors, or other physics-based constraints interfere with the target object mimicking the reference object. A tracking agent, along with a corresponding neural network, can be used to compensate and modify the movements of the target object. Should the target object diverge significantly from the reference object, such as falling down, a recovery agent, along with a corresponding neural network, can be used to move the target object back into an approximate alignment with the reference object before resuming the tracking process.

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: A cable driving a large system such as cable driven machines, cable cars or tendons in a human or robot is typically modeled as a large number of small segments that are connected via joints. The two main difficulties with this approach are satisfying the inextensibility constraint and handling the typically large mass ratio between the small segments and the larger objects they connect. This disclosure introduces a more effective approach to solving these problems. The introduced approach simulates the effect of a cable instead of the cable itself using a new type of distance constraint called ‘cable joint’ that changes both its attachment points and its rest length dynamically. The introduced approach models a cable connecting a series of objects as a sequence of cable joints, reducing the complexity of the simulation from the order of the number of segments in the cable to the number of connected objects.

Abstract: Embodiments of the present invention provide a novel method and discretization for animating water waves. The approaches disclosed combine the flexibility of a numerical approach to wave simulation with the stability and visual detail provided by a spectrum-based approach to provide Eulerian methods for simulating large-scale oceans with highly detailed wave features. A graphics processing unit stores a one-dimensional texture referred to as a wave profile buffer that stores pre-computed results at a number of discrete sample points for performing wave height evaluation. The water surface is rendered according to water height values computed using the wave profile, accounting for advection, spatial diffusion, angular diffusion, boundary reflections, and dissipation.

Abstract: Embodiments of the present invention provide a method for simulating deformable solids undergoing large plastic deformation and topological changes using shape matching. Positional information for particles and orientation information from clusters is used to simulate deformable solids represented by particles. Each visual vertex stores references to particles that influence the vertex, and stores the local position of the particles. A two-step method interpolates orientation from clusters to particles, and uses the orientation and position of particles to skin the visual mesh vertices. This results in a fast method that can reproduce rotation and does not require the visual mesh vertex to be located within a convex hull of particles.