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: The disclosed method for training machine learning models for object generation includes performing, based on object data, one or more operations to train an untrained machine learning model to generate a trained machine learning model that comprises a trained encoder and a trained decoder, wherein the trained machine learning model is trained to generate an object surface representation, performing, based on the object data and natural language data, one or more operations to train an untrained diffusion model to generate a trained diffusion model, where the trained diffusion model is trained to generate an object geometry embedding, and where the trained diffusion model and the trained decoder are used to generate a virtual object based on natural language input.

Abstract: The disclosed method for generating a virtual object includes processing a language embedding associated with a natural language description of an object using a trained diffusion model to generate a first object geometry embedding, processing the first object geometry embedding using a trained decoder to generate an object surface representation, and converting the object surface representation into a first object geometry of the virtual object.

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 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: Apparatuses, systems, and techniques for generating a clothed three-dimensional (3D) avatar character from a text prompt and enabling smooth animation through physics or neural simulators. In at least one embodiment, a clothed 3D avatar is generated through body layer modeling and garment layer modeling based on text descriptions. The outputs from the body layer and garment layer modeling are combined to generate an animation-ready, clothed 3D avatar.

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: In various examples, determining angle-weighted normals for content processing systems and applications is described herein. Systems and methods are disclosed that determine an angle-weighted normal, such as during a traversal of a bounding volume hierarchy (BVH) of a three-dimensional (3D) object, and then use the angle-weighted normal to determine whether a query point is located inside or outside of the 3D object. To determine the angle-weighted normal, triangles that potentially include a closest point to the query point may be identified, such as during the traversal of the BVH to identify the closest point on the 3D object to the query point. The potential triangles may then be analyzed to determine one or more triangles for which the closest point is located. Additionally, one or more weights and/or one or more surface normals associated with the triangle(s) may be used to calculate the angle-weighted normal.

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: 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 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: In various examples, a technique for performing a particle-based simulation includes propagating, via a first portion of a machine learning model, a first plurality of features associated with a plurality of particles across a hierarchy of grids, wherein the hierarchy of grids includes a first grid having a first grid spacing and a second grid having a second grid spacing that is greater than the first grid spacing. The technique also includes propagating, via a second portion of the machine learning model, a second plurality of features across the hierarchy of grids to the plurality of particles. The technique further includes determining a plurality of accelerations associated with the plurality of particles based on the second set of features propagated to the plurality of particles, and generating a simulation associated with the plurality of particles based on the plurality of accelerations.

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).