Abstract: Apparatuses, systems, and techniques to generate a prompt for one or more machine learning processes. In at least one embodiment, the machine learning process(es) generate(s) a plan to perform a task (identified in the prompt) that is to be performed by an agent (real world or virtual).

Abstract: In various examples, robust trajectory predictions against adversarial attacks in autonomous machines and applications are described herein. Systems and methods are disclosed that perform adversarial training for trajectory predictions determined using a neural network(s). In order to improve the training, the systems and methods may devise a deterministic attach that creates a deterministic gradient path within a probabilistic model to generate adversarial samples for training. Additionally, the systems and methods may introduce a hybrid objective that interleaves the adversarial training and learning from clean data to anchor the output from the neural network(s) on stable, clean data distribution. Furthermore, the systems and methods may use a domain-specific data augmentation technique that generates diverse, realistic, and dynamically-feasible samples for additional training of the neural network(s).

Abstract: Apparatuses, systems, and techniques to generate trajectory data for moving objects. In at least one embodiment, adversarial trajectories are generated to evaluate a trajectory prediction model and are based, at least in part, on a differentiable dynamic model.

Abstract: In various examples, techniques for generating simulations for autonomous machines and applications are described herein. Systems and methods are disclosed that use various models to generate simulations. For instance, a first model(s) may process input data, such as input data representing maps indicating the locations of objects and state history of the objects within the environment, to determine navigation goals for the objects. Additionally, a second model(s) may then process the input data and data representing the navigation goals in order to determine possible trajectories (e.g., action samples) for the objects within the environment. Furthermore, a third model(s) may process the input data to predict trajectories of the objects within the environment. The systems and methods may then use at least the possible trajectories and the predicted trajectories to simulate the motion (e.g., one or more trajectories) of one or more of the objects.

Abstract: A three-dimensional bounding box is determined from a two-dimensional image and a point cloud. A feature vector associated with the image and a feature vector associated with the point cloud may be passed through a neural network to determine parameters of the three-dimensional bounding box. Feature vectors associated with each of the points in the point cloud may also be determined and considered to produce estimates of the three-dimensional bounding box on a per-point basis.

Abstract: A three-dimensional bounding box is determined from a two-dimensional image and a point cloud. A feature vector associated with the image and a feature vector associated with the point cloud may be passed through a neural network to determine parameters of the three-dimensional bounding box. Feature vectors associated with each of the points in the point cloud may also be determined and considered to produce estimates of the three-dimensional bounding box on a per-point basis.

Abstract: A three-dimensional bounding box is determined from a two-dimensional image and a point cloud. A feature vector associated with the image and a feature vector associated with the point cloud may be passed through a neural network to determine parameters of the three-dimensional bounding box. Feature vectors associated with each of the points in the point cloud may also be determined and considered to produce estimates of the three-dimensional bounding box on a per-point basis.

Abstract: A three-dimensional bounding box is determined from a two-dimensional image and a point cloud. A feature vector associated with the image and a feature vector associated with the point cloud may be passed through a neural network to determine parameters of the three-dimensional bounding box. Feature vectors associated with each of the points in the point cloud may also be determined and considered to produce estimates of the three-dimensional bounding box on a per-point basis.