Abstract: A computing system identifies a trajectory example generated by a human operator. The trajectory example includes trajectory information of the human operator while performing a task to be learned by a control system of the computing system. Based on the trajectory example, the computing system trains the control system to perform the task exemplified in the trajectory example. Training the control system includes generating an output trajectory of a robot performing the task. The computing system identifies an updated trajectory example generated by the human operator based on the trajectory example and the output trajectory of the robot performing the task. Based on the updated trajectory example, the computing system continues to train the control system to perform the task exemplified in the updated trajectory example.

Abstract: An electric vehicle body can be configured to minimize the entry of foreign matters in a replaceable battery pack region while facilitating battery pack replacement. An electric vehicle body can include first and second battery packs replaceably mounted on a chassis frame, a cover bracket extending on one side of the first battery pack, and an undercover extending on one side of the second battery pack adjacent to the first battery pack and overlapping the cover bracket to seal off a gap between the first battery pack and the second battery pack.

Abstract: The present disclosure relates to an apparatus for training and causing autonomous driving control of a vehicle. The apparatus may comprise at least one processor, and a memory storing instructions, when executed by the at least one processor, cause the apparatus to obtain, based on a depth map obtained from a cluster of points at a target time point, a depth distribution map, obtain, based on an input image that is associated with the target time point and that is applied to a monocular depth estimation (MDE) model, a depth estimation map, update, based on a loss function group applied to the MDE model, a plurality of weights included in the MDE model, wherein the loss function group may comprise a first loss function that is obtained based on the depth distribution map and the depth estimation map, and output a signal indicating the updated plurality of weights.

Abstract: Proposed are a flow-through reactor for conversion of carbon dioxide and a method for conversion of carbon dioxide using the flow-through reactor. Carbon dioxide and a catholyte are separately supplied, and a reference electrode having a large volume is capable of being mounted on an electrolyte pocket without increasing resistance since a structure of the electrolyte pocket is improved.

Abstract: A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.

Abstract: A learning device, a learning method thereof, a test device using the same, and a test method using the same are provided. The learning device may obtain a target image and a source image, generate an estimated depth map based on the target image via a first network, generate pose change information corresponding to a pose change between the target image and the source image, generate a composite image corresponding to the target image, determine a first loss based on the composite image and the target image, and determine a second loss, and back-propagate the first loss and the second loss and update a parameter of the first network and a parameter of the second network.

Abstract: A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.

Abstract: A learning device is introduced. The device may comprise a processor, and memory storing instructions that, when executed by the processor, may cause the device to obtain at least one first depth map based on at least one piece of cloud data associated with surrounding environment information, and at least one first image associated with the at least one first depth map, determine, based on the at least one first depth map and the at least one first image, variance estimation information indicating a variance between the at least one first depth map and the at least one first image, back-propagate a variance loss based on the first variance estimation information, and variance ground truth (GT) information associated with the first variance estimation information, and update, based on the back-propagated variance loss, a parameter associated with determining the first variance estimation information.

Abstract: An apparatus for estimating a depth is introduced. The apparatus may comprise a camera configured to capture an image may comprise an object, and a processor configured to perform, based on a deep learning model, a deep learning process associated with the image, obtain, based on the deep learning process associated with the image, a first depth value, obtain a partial image by masking a partial region of the image, perform, based on the deep learning model, a deep learning process associated with the partial image, obtain, based on the deep learning process associated with the partial image, a second depth value, train the deep learning model to reduce a deviation between the first depth value and the second depth value, and estimate, based on the trained deep learning model, a depth of the object.

Abstract: The present disclosure relates to a thermoplastic resin composition, including 35 to 65% by weight of a polyamide resin (A); 18 to 40% by weight of glass fiber (B); 10 to 30% by weight of a composite flame retardant (C); 0.06 to 0.4% by weight of a phosphite-based thermal stabilizer (D); and 0.1 to 0.7% by weight of a copper-based composite thermal stabilizer (E), a method of preparing the thermoplastic resin composition, and a molded article including the thermoplastic resin composition.

Abstract: A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.

Abstract: A computing system identifies a trajectory example generated by a human operator. The trajectory example includes trajectory information of the human operator while performing a task to be learned by a control system of the computing system. Based on the trajectory example, the computing system trains the control system to perform the task exemplified in the trajectory example. Training the control system includes generating an output trajectory of a robot performing the task. The computing system identifies an updated trajectory example generated by the human operator based on the trajectory example and the output trajectory of the robot performing the task. Based on the updated trajectory example, the computing system continues to train the control system to perform the task exemplified in the updated trajectory example.

Abstract: A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.

Abstract: A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.

Abstract: The method for performing endoscopic examination according to an exemplary embodiment of the present invention may include the steps of obtaining an endoscopic image and a 3D model of a target organ; identifying location information of an endoscope on the 3D model in real time based on simultaneous localization and mapping (SLAM); classifying a plurality of areas constituting the 3D model into examination areas and unexamined areas by using the endoscopic image and the location information; and providing feedback information based on the examination areas and unexamined areas.

Abstract: Aspects of the disclosed technology provide a computational model that utilizes machine learning for detecting errors during a manual assembly process and determining a sequence of steps to complete the manual assembly process in order to mitigate the detected errors. In some implementations, the disclosed technology evaluates a target object at a step of an assembly process where an error is detected to a nominal object to obtain a comparison. Based on this comparison, a sequence of steps for completion of the assembly process of the target object is obtained. The assembly instructions for creating the target object are adjusted based on this sequence of steps.

Abstract: A training data selection device for selecting training data and a training data selection method therefor are provided. The training data selection device includes a depth estimation network that applies depth estimation calculation to an input image obtained in real time to output depth distribution information corresponding to the input image. The device includes a vulnerability output device that outputs depth estimation vulnerability corresponding to the input image with reference to the depth distribution information. The device includes a training data acquisition support device that stores the input image and specific point cloud data corresponding to the input image as new training data in a certain storage space or transmits the input image and the specific point cloud data to another device, when it is determined that the depth estimation vulnerability is greater than or equal to a predetermined threshold.

Abstract: A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.

Abstract: A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.

Abstract: A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.