Abstract: An example apparatus includes: a supply terminal; a first inductor having a first terminal and a second terminal; a second inductor having a first terminal and a second terminal, the first terminal of the second inductor coupled to the supply terminal and the first terminal of the first inductor; a first resistor having a first terminal and a second terminal, the first terminal of the first resistor coupled to the second terminal of the first inductor; a second resistor having a first terminal and a second terminal, the first terminal of the second resistor coupled to the second terminal of the second inductor; a first transmission line having a first terminal and a second terminal, the first terminal of the first transmission line coupled to the second terminal of the first resistor; a second transmission line having a first terminal and a second terminal.

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: An example apparatus includes: a supply terminal; a first inductor having a first terminal and a second terminal; a second inductor having a first terminal and a second terminal, the first terminal of the second inductor coupled to the supply terminal and the first terminal of the first inductor; a first resistor having a first terminal and a second terminal, the first terminal of the first resistor coupled to the second terminal of the first inductor; a second resistor having a first terminal and a second terminal, the first terminal of the second resistor coupled to the second terminal of the second inductor; a first transmission line having a first terminal and a second terminal, the first terminal of the first transmission line coupled to the second terminal of the first resistor; a second transmission line having a first terminal and a second terminal.

Abstract: Systems and techniques are described related to training one or more machine learning models for use in control of a robot. In at least one embodiment, one or more machine learning models are trained based at least on simulations of the robot and renderings of such simulations—which may be performed using one or more ray tracing algorithms, operations, or techniques.