Abstract: Provided are flexible hybrid interconnect circuits and methods of forming thereof. A flexible hybrid interconnect circuit comprises multiple conductive layers, stacked and spaced apart along the thickness of the circuit. Each conductive layer comprises one or more conductive elements, one of which is operable as a high frequency (HF) signal line. Other conductive elements, in the same and other conductive layers, form an electromagnetic shield around the HF signal line. Some conductive elements in the same circuit are used for electrical power transmission. All conductive elements are supported by one or more inner dielectric layers and enclosed by outer dielectric layers. The overall stack is thin and flexible and may be conformally attached to a non-planar surface. Each conductive layer may be formed by patterning the same metallic sheet. Multiple pattern sheets are laminated together with inner and outer dielectric layers to form a flexible hybrid interconnect circuit.

Abstract: A method for performing a task by a robotic device includes mapping a group of task image pixel descriptors associated with a first group of pixels in a task image of a task environment to a group of teaching image pixel descriptors associated with a second group of pixels in a teaching image based on positioning the robotic device within the task environment. The method also includes determining a relative transform between the task image and the teaching image based on mapping the plurality of task image pixel descriptors. The relative transform indicates a change in one or more of points of 3D space between the task image and the teaching image. The method also includes performing the task associated with the set of parameterized behaviors based on updating one or more parameters of a set of parameterized behaviors associated with the teaching image based on determining the relative transform.

Abstract: Provided are flexible hybrid interconnect circuits and methods of forming thereof. A flexible hybrid interconnect circuit comprises multiple conductive layers, stacked and spaced apart along the thickness of the circuit. Each conductive layer comprises one or more conductive elements, one of which is operable as a high frequency (HF) signal line. Other conductive elements, in the same and other conductive layers, form an electromagnetic shield around the HF signal line. Some conductive elements in the same circuit are used for electrical power transmission. All conductive elements are supported by one or more inner dielectric layers and enclosed by outer dielectric layers. The overall stack is thin and flexible and may be conformally attached to a non-planar surface. Each conductive layer may be formed by patterning the same metallic sheet. Multiple pattern sheets are laminated together with inner and outer dielectric layers to form a flexible hybrid interconnect circuit.

Abstract: The disclosed computer-implemented method may include generating, using a machine-learning model of a computing device, a set of antenna designs. The method may also include tokenizing, by the computing device, each antenna design in the generated set of antenna designs. Additionally, the method may include predicting, by the machine-learning model of the computing device, a frequency response for each tokenized antenna design. Furthermore, the method may include comparing, by the computing device, the frequency response for each tokenized antenna design. Finally, the method may include selecting, by the computing device based on the comparison, an antenna design that meets a performance threshold for the frequency response. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A method for 3D object perception is described. The method includes extracting features from each image of a synthetic stereo pair of images. The method also includes generating a low-resolution disparity image based on the features extracted from each image of the synthetic stereo pair images. The method further includes predicting, by a trained neural network, a feature map based on the low-resolution disparity image and one of the synthetic stereo pair of images. The method also includes generating, by a perception prediction head, a perception prediction of a detected 3D object based on the feature map predicted by the trained neural network.

Abstract: A method for training a neural network to perform 3D object manipulation is described. The method includes extracting features from each image of a synthetic stereo pair of images. The method also includes generating a low-resolution disparity image based on the features extracted from each image of the synthetic stereo pair of images. The method further includes generating, by the neural network, a feature map based on the low-resolution disparity image and one of the synthetic stereo pair of images. The method also includes manipulating an unknown object perceived from the feature map according to a perception prediction from a prediction head.

Abstract: A method for training an object detection system includes estimating a location of a first object in an environment based on a density cluster map generated from a plurality of images of the environment. The method also includes generating one or more negative training samples of the first object in the environment based on the plurality of images, each of the one or more negative training samples corresponding to a second object at a location in the environment that is different than the estimated location of the first object. The method further includes generating positive training samples from a set of images of the first object. The method also includes training the object detection system to detect the first object based on the positive training samples and the negative training sample.

Abstract: A method for controlling a robotic device is presented. The method includes capturing an image corresponding to a current view of the robotic device. The method also includes identifying a keyframe image comprising a first set of pixels matching a second set of pixels of the image. The method further includes performing, by the robotic device, a task corresponding to the keyframe image.

Abstract: A method for performing a task by a robotic device includes mapping a group of task image pixel descriptors associated with a first group of pixels in a task image of a task environment to a group of teaching image pixel descriptors associated with a second group of pixels in a teaching image based on positioning the robotic device within the task environment. The method also includes determining a relative transform between the task image and the teaching image based on mapping the plurality of task image pixel descriptors. The relative transform indicates a change in one or more of points of 3D space between the task image and the teaching image. The method also includes performing the task associated with the set of parameterized behaviors based on updating one or more parameters of a set of parameterized behaviors associated with the teaching image based on determining the relative transform.

Abstract: A method for generating positive and negative training samples is presented. The method includes identifying false positive images of an object based on multiple images of an environment. The method also includes generating positive training samples from a set of images of the object. The method further includes generating a negative training sample from the false positive image. The method still further includes training an object detection system based on the positive training samples and the negative training sample.

Abstract: System, methods, and other embodiments described herein relate to single-shot multi-object three-dimensional (3D) shape reconstruction and categorical six-dimensional (6D) pose and size estimation. In one embodiment, a method includes inferring a heatmap based upon a feature pyramid, where the feature pyramid is generated based upon a red green blue depth (RGB-D) image that includes objects. The method further includes sampling a 3D parameter map at locations corresponding to peaks in the heatmap, where the 3D parameter map is inferred based upon the feature pyramid, and where the locations include latent shape codes, 6D poses, and one-dimensional (1D) scales. The method further includes generating point clouds based upon the latent shape codes, the 6D poses, and the 1D scales.

Abstract: A system includes a memory module configured to store image data captured by a camera and an electronic controller communicatively coupled to the memory module. The electronic controller is configured to receive image data captured by the camera, implement a neural network trained to predict a drivable portion in the image data of an environment. The neural network predicts the drivable portion in the image data of the environment. The electronic controller is configured to implement a support vector machine. The support vector machine determines whether the predicted drivable portion of the environment output by the neural network is classified as drivable based on a hyperplane of the support vector machine and output an indication of the drivable portion of the environment.

Abstract: A method for controlling a robotic device is presented. The method includes positioning the robotic device within a task environment. The method also includes mapping descriptors of a task image of a scene in the task environment to a teaching image of a teaching environment. The method further includes defining a relative transform between the task image and the teaching image based on the mapping. Furthermore, the method includes updating parameters of a set of parameterized behaviors based on the relative transform to perform a task corresponding to the teaching image.

Abstract: A multi-charger, serially operated electrical vehicle (EV) charging system, contains a Power Control System (PCS) providing DC power. A plurality of EV chargers is serially power-connected to each other, wherein the first EV charger is connected to the PCS. There are sets of relays in at least the first EV charger, wherein a first set of the set of relays, when activated, is configured to supply power to a respective charging cable of the EV charger, and a second set of the set of relays, when activated, is configured to supply power to a next-serially connected EV charger. The sets of relays contain auxiliary contacts providing relay status information. A hardware logic prevents the first and second sets of relays from simultaneously being activated, allowing only one EV charger of the plurality of EV chargers to charge at a time.

Abstract: A method for controlling a robotic device is presented. The method includes capturing an image corresponding to a current view of the robotic device. The method also includes identifying a keyframe image comprising a first set of pixels matching a second set of pixels of the image. The method further includes performing, by the robotic device, a task corresponding to the keyframe image.

Abstract: A method for controlling a robotic device is presented. The method includes capturing an image corresponding to a current view of the robotic device. The method also includes identifying a keyframe image comprising a first set of pixels matching a second set of pixels of the image. The method further includes performing, by the robotic device, a task corresponding to the keyframe image.

Abstract: A method for generating positive and negative training samples is presented. The method includes identifying false positive images of an object based on multiple images of an environment. The method also includes generating positive training samples from a set of images of the object. The method further includes generating a negative training sample from the false positive image. The method still further includes training an object detection system based on the positive training samples and the negative training sample.

Abstract: A method for training a deep neural network of a robotic device is described. The method includes constructing a 3D model using images captured via a 3D camera of the robotic device in a training environment. The method also includes generating pairs of 3D images from the 3D model by artificially adjusting parameters of the training environment to form manipulated images using the deep neural network. The method further includes processing the pairs of 3D images to form a reference image including embedded descriptors of common objects between the pairs of 3D images. The method also includes using the reference image from training of the neural network to determine correlations to identify detected objects in future images.

Abstract: A system includes a memory module configured to store image data captured by a camera and an electronic controller communicatively coupled to the memory module. The electronic controller is configured to receive image data captured by the camera, implement a neural network trained to predict a drivable portion in the image data of an environment. The neural network predicts the drivable portion in the image data of the environment. The electronic controller is configured to implement a support vector machine. The support vector machine determines whether the predicted drivable portion of the environment output by the neural network is classified as drivable based on a hyperplane of the support vector machine and output an indication of the drivable portion of the environment.

Abstract: An apparatus is described. The apparatus includes an electro-mechanical interface having angled signal interconnects, wherein, the angling of the signal interconnects is to reduce noise coupling between the angled signal interconnects.