Abstract: A method includes receiving data from one or more sensors, detecting, from the received data, one or more activities of a person in a space, converting the detected one or more activities into a natural language narration; extracting one or more items from a map corresponding to the space; determining a relevancy score for each of the extracted one or more items based on an output of a language model that receives as input a combination of the narration with a binding sequence; correlating the extracted one or more items with one or more locations on the map based on the determined relevancy for each of the extracted one or more items; and outputting a control signal for controlling a movement of one more devices based on the correlation of the extracted one or more items with the one or more locations on the map.

Abstract: A tactile sensing system of a robot may include: a plurality of piezoelectric elements disposed at an object, and including a transmission (TX) piezoelectric element and a reception (RX) piezoelectric element; and at least one processor configured to: control the TX piezoelectric element to generate an acoustic wave having a chirp spread spectrum (CSS) at every preset time interval, along a surface of the object; receive, via the RX piezoelectric element, an acoustic wave signal corresponding to the generated acoustic wave; select frequency bands from a plurality of frequency bands of the acoustic wave signal; and estimate a location of a touch input on the surface of the object by inputting the acoustic wave signal of the selected frequency bands into a neural network configured to provide a touch prediction score for each of a plurality of predetermined locations on the surface of the object.

Abstract: A method of generating a composite map from image data and spatial map data may include: acquiring a spatial map of an environment; acquiring a plurality of images of a portion of the environment; generating a three-dimensional (3D) image of the portion of the environment using the plurality of images; identifying a floor in the 3D image; generating a synthetic spatial map of the portion of the environment based on the floor in the 3D image; determining a location of the portion of the environment within the spatial map by identifying a region of the spatial map that corresponds to the synthetic spatial map; and generating a composite map by associating the 3D image with the location within the spatial map of the portion of the environment.

Abstract: A system and a method for generating a trajectory of a target device from a current position to a goal position within an environment is provided. The method may include: inputting physical workspace information associated with the environment in which the target device, to a first neural network to obtain a set of weights representing a cost-to-go function that defines a cost-to-go function relating to a length of a collision-free path from one position to the goal position; configuring a second neural network based on the set of weights; identifying a next position of the target device based on the current position and a motion control input of the target; and inputting the identified next position of the target device and the goal position to the second neural network to identify the trajectory to the goal position, and the motion control input corresponding to the trajectory.

Abstract: A tactile sensing system of a robot may include: a plurality of piezoelectric elements disposed at an object, and including a transmission (TX) piezoelectric element and a reception (RX) piezoelectric element; and at least one processor configured to: control the TX piezoelectric element to generate an acoustic wave having a chirp spread spectrum (CSS) at every preset time interval, along a surface of the object; receive, via the RX piezoelectric element, an acoustic wave signal corresponding to the generated acoustic wave; select frequency bands from a plurality of frequency bands of the acoustic wave signal; and estimate a location of a touch input on the surface of the object by inputting the acoustic wave signal of the selected frequency bands into a neural network configured to provide a touch prediction score for each of a plurality of predetermined locations on the surface of the object.

Abstract: A method of estimating an amount of liquid poured from a container includes receiving a tactile signal detected by at least one tactile sensor of an end effector holding the container containing the liquid; receiving a joint signal including an angular position, angular velocity, and torque of a joint connected to the end effector; and estimating, based on the tactile signal and the joint signal, the amount of liquid poured from the container.

Abstract: An computing system includes: receiving an image that captures a hand and an object; estimating a three-dimensional (3D) mesh of the hand based on the image; estimating a two-dimensional (2D) segment of the object based on the image; estimating a 3D feature representing a correlation of the hand and the object, based on the 3D mesh of the hand and the 2D segment of the object; and generating a first set of output parameters for a first 3D query point, based on the 3D feature representing the correlation of the hand and the object.

Abstract: Methods and devices for processing image data for scene completion, including receiving an original image from an original viewpoint corresponding to a first direction, wherein the original image includes an object; obtaining a first image from a new viewpoint corresponding to a second direction different from the first direction by rotating the original image based on 3-dimensional (3D) information generated from 2-dimensional (2D) information which is obtained from the original image; determining an area within the first image for generating a second surface of the object based on depth information about a depth between the object and the background of the original image, wherein the determined area is expected to include an object area; and obtaining a second image by inputting the first image and the determined area to an artificial intelligence (AI) inpainting model, wherein the AI inpainting model generates the second surface of the object which occupies a portion of the determined area in the second im

Abstract: An electronic device for manipulating a target object, including: a camera; a memory; and at least one processor configured to: obtain a first image of the target object, wherein the first image is captured by the camera, provide the first image and a target image to an artificial intelligence (AI) model to obtain relative pose information, based on the obtained relative pose information, generate a similarity value, and generate a control command based on the similarity value, wherein based on the similarity value being greater than a threshold value, the control command includes a movement command for moving a manipulator associated with the electronic device from a first position to a second position, and wherein based on the similarity value being less than or equal to the threshold value, the control command includes a manipulation command for manipulating the target object using the manipulator.

Abstract: In some embodiments, an apparatus for performing reactive positioning of a robot gripper includes one or more fingers disposed on an end-effector of the robot, a signal processing circuit, a memory storing instructions, and a processor. Each of the one or more fingers includes a transducer configured to generate vibrational energy based on an input signal, and convert an acoustic reflection of the vibrational energy from an object into a voltage signal. The signal processing circuit is configured to provide the input signal to each transducer, and perform signal processing on the voltage signal of each transducer resulting in reflection data. The processor is configured to execute the instructions to perform pre-touch proximity detection on the reflection data, perform grasp positioning on the reflection data, perform contact detection from the reflection data, and provide, to the robot, results of the pre-touch proximity detection, the grasp positioning, and the contact detection.

Abstract: The present disclosure provides methods, apparatuses, and computer-readable mediums for evaluating a reliability of three-dimensional shape predictions.

Abstract: Systems, methods, and apparatuses for controlling a robot including a manipulator, including: determining three-dimensional (3D) geometry information about a target object based on an image of the target object; determining 3D geometry information about a scene in which the target object is to be placed based on at least one image of the scene; obtaining affordance information by providing the 3D geometry information about the target object and the 3D geometry information about the scene to at least one neural network model; commanding the robot to grasp the target object using the manipulator according to a grasp orientation corresponding to the affordance information; and commanding the robot to position the manipulator according to a placement direction corresponding to the affordance information in order to place the target object at a location in the scene.

Abstract: A method of predicting occupancy of unseen areas in a region of interest (ROI) includes obtaining a depth image of the ROI, the depth image being captured from a first height; generating an occupancy map based on the obtained depth image, the occupancy map comprising an array of cells corresponding to locations in the ROI; and generating an inpainted map by inputting the occupancy map into a trained inpainting network, the inpainted map comprising an array of cells corresponding to the ROI, and wherein the inpainting network is trained by comparing an output of the inpainting network, based on inputting a training depth image taken from the first height, to a ground truth map, the ground truth map being based on a combination of the training depth image and a depth image taken at a height different than the first height.

Abstract: A detection system includes a filter and a detector. The filter receives input packets from one or more dynamic vision sensors (DVSs) deployed in an area. The filter reduces the bandwidth of the input packets to produce compressed data packets while maintaining good end-to-end detection capability. The compressed data packets are transmitted to a detector in a low bandwidth transmission. The detector may collect compressed data packets from many filters. The detector determines if an object of interest is represented in the information of the input packets. If an object of interest is present, the detector activates an alarm, sends a notification to a person associated with the area and/or stores an indication in a database.

Abstract: An apparatus for estimating a proximity direction of an obstacle includes an acoustic transmitter attached to a surface of the apparatus; a first acoustic receiver spaced apart from the surface of the apparatus; a second acoustic receiver spaced apart from the surface of the apparatus; and at least one processor configured to: control the acoustic transmitter to generate an acoustic wave along the surface; obtain first and second proximity direction signals based on first and second acoustic wave signals corresponding to the generated acoustic wave; and estimate a proximity direction of the obstacle with respect to the apparatus based on the first and second proximity direction signals.

Abstract: A depth image is used to obtain a three dimensional (3D) geometry of an object as an object mesh. The object mesh is obtained using an object shell representation. The object shell representation is based on a series of depth images denoting the entry and exit points on the object surface that camera rays would pass through. Given a set of entry points in the form of a masked depth image of an object, an object shell (an entry image and an exit image) is generated. Since entry and exit images contain neighborhood information given by pixel adjacency, the entry and exit images provide partial meshes of the object which are stitched together in linear time using the contours of the entry and exit images. A complete object mesh is provided in the camera coordinate frame.

Abstract: A method of collision localization on a robotic device includes obtaining audio signals from a plurality of acoustic sensors spaced apart along the robotic device; identifying, based on a collision being detected, a strongest audio signal; identifying a primary onset time for an acoustic sensor producing the strongest audio signal, the primary onset time being a time at which waves propagating from the collision reach the acoustic sensor producing the strongest audio signal; generating a virtual onset time set, by shifting a calibration manifold, based on the identified primary onset time, the calibration manifold representing relative onset times from evenly spaced marker locations on the robotic device to the plurality of acoustic sensors; determining scores for the marker locations based a standard deviation of elements in the virtual onset time set; and estimating a location of the collision based on a highest score of the determined scores.

Abstract: A method may include obtaining a set of events, of a set of pixels of a dynamic vision sensor, associated with an object; determining a set of voltages of the set of pixels, based on the set of events; generating a set of images, based on the set of voltages of the set of pixels; inputting the set of images into a first neural network configured to output a visual motion estimation of the object; inputting the set of images into a second neural network configured to output a confidence score of the visual motion estimation output by the first neural network; obtaining the visual motion estimation of the object and the confidence score of the visual motion estimation of the object, based on inputting the set of images into the first neural network and the second neural network; and providing the visual motion estimation of the object and the confidence score.

Abstract: A pose of a person is estimated using an image and audio impulse responses. The image represents a 2D scene including the person. The audio impulse responses are obtained with the present absent and present in an environment. The pose is reconstructed based on the image and the one or more audio impulse responses. The pose is a metric scale human pose.

Abstract: A method for generating a trajectory of a robot from a first configuration to a second configuration within an environment while steering away from obstacles may include obtaining physical workspace information associated with the environment in which the robot is configured to operate; obtaining, using a first neural network, a set of weights of a second neural network that is configured to generate a set of values associated with a set of configurations of the robot with respect to the second configuration; obtaining, by applying the set of weights to the second neural network, the set of values associated with the set of configurations of the robot with respect to the second configuration; and generating the trajectory of the robot from the first configuration to the second configuration within the environment, based on the set of values.