Abstract: A control system for controlling a set of actuators of a system. The control system comprising a switcher configured to select a subset of setpoints from a set of setpoints that control the corresponding set of actuators. An extremum-seeking controller (ESC) configured to perturb a subset of setpoints at each iteration based on a probabilistic distribution of partial gradients of a cost function relating values of the subset of setpoints to a cost of operation of the system. A stochastic gradient estimator is configured to estimate a full gradient of the cost function and update the estimation of the full gradient based on the probabilistic distribution of the partial gradients generated at each ESC iteration. A feedback controller is configured to drive a state of the subset of actuators of the system towards the corresponding perturbed subset of setpoints.

Abstract: A system for trajectories imitation for robotic manipulators is provided. The system includes an interface configured to receive a plurality of task descriptions, wherein the interface is configured to communicate with a real-world robot, a memory to store computer-executable programs including a robot simulator, a training module and a transfer module, and a processor, in connection with the memory. The processor is configured to perform training using the training module, for the task descriptions on the robot simulator, to produce a plurality of source policy with subgoals for the task descriptions. The processor performs training using the training module, for the task descriptions on the real-world robot, to produce a plurality of target policy with subgoals for the task descriptions, and update the parameters of the transfer module from corresponding trajectories with the subgoals for the robot simulator and real-world robot.

Abstract: An image forming system for constructing a whole large image of an object is provided. The image forming system includes an interface configured to sequentially acquire images of partial areas of the object obtained by a camera, wherein two adjacent images of the partial areas include overlap portions, wherein the sequential images correspond to a three dimensional (3D) surface of the object, wherein the geometrical information (geometrical parameters) of the object is provided, wherein an initial pose of the camera is provided.

Abstract: A system a method for controlling an operation of HVAC system is provided. The system comprises at least one processor; and memory having instructions stored thereon that, when executed by the at least one processor, cause the system to receive data indicative of the operation of the HVAC system and optimize a cost function to reduce a difference between a full thermal state of the conditioned environment simulated from the received data based on a physical model of airflow including a partial differential equation (PDE) and a thermal state of the conditioned environment reconstructed by an observer using a reduced order model (ROM) of airflow including an ordinary differential equation (ODE) from values of the full thermal state at a set of locations, wherein optimization of the cost function is a multivariable optimization of a structure of the observer and a structure of the ODE.

Abstract: Data communications and storage systems require error control techniques to be transferred successfully without failure. Polar coding has been used as a state-of-the-art forward error correction code for such an error control technique. However, the conventional decoding based on successive cancellation has a drawback in its poor performance and long latency to complete. Because the factor graph of polar codes has a lot of short cycles, a parallelizable belief propagation decoding also does not perform well. The method and system of the present invention provide a way to resolve the issues by introducing a protograph lifting expansion for a polar coding family so that highly parallelizable decoding is realized to achieve a high coding gain and high throughput without increasing the computational complexity and latency. The invention enables an iterative message passing to work properly by eliminating short cycles through a hill-climbing optimization of frozen bits allocation and permutation.

Abstract: A linguistic system for transcribing an input, where the linguistic system comprises a processor configured to execute a neural network multiple times while varying weights of at least some nodes of the neural network to produce multiple transcriptions of the input. Further, determine a distribution of pairwise distances of the multiple transcriptions; determine a legitimacy of the input based on the distribution; and transcribe the input using stored weights of the nodes of the neural network when the input is determined as legitimate to produce a final transcription of the input.

Abstract: A control system for controlling a set of actuators of a system. The control system comprising a switcher configured to select a subset of setpoints from a set of setpoints that control the corresponding set of actuators. An extremum-seeking controller (ESC) configured to perturb a subset of setpoints at each iteration based on a probabilistic distribution of partial gradients of a cost function relating values of the subset of setpoints to a cost of operation of the system. A stochastic gradient estimator is configured to estimate a full gradient of the cost function and update the estimation of the full gradient based on the probabilistic distribution of the partial gradients generated at each ESC iteration. A feedback controller is configured to drive a state of the subset of actuators of the system towards the corresponding perturbed subset of setpoints.

Abstract: System and method for generating verisimilar images from real depth images. Train a generative adversarial neural network (GAN) by accessing test depth images having identical instances as instances of a real depth image. Input the test depth images in the generator to generate estimated depth images representing an implicit three-dimensional model of the object. Input, each estimated depth image into a discriminator to obtain a loss and into a pose encoder to obtain a matching loss. Iteratively repeat processes until the losses are minimized to a threshold, to end training. Identify the instances in the real image using the trained GAN pose encoder, to produce a pose transformation matrix for each instance in the real image. Identify pixels in the depth images corresponding to the instances of the real image and merge the pixels for the depth images to form an instance segmentation map for the real depth image.

Abstract: A system for performing a task according to a reference trajectory is provided. The system includes at least one actuator configured to change a state of the system according to a control input, and a memory configured to store a model of dynamics of the system including a known part of the dynamics of the system as a function of the state of the system and the control input to the system and an unknown part of the dynamics of the system as a function of the state of the system, wherein the unknown part of the dynamics of the system is represented by parameters of a probabilistic distribution including a first-order moment and a second-order moment of the probabilistic distribution. The system also includes a control system configured to recursively determine and submit the control input to the actuator to change the state of the system.

Abstract: A robot for performing an assembly operation is provided. The robot comprises a processor configured to determine a control law for controlling a plurality of motors of the robot to move a robotic arm according to an original trajectory, execute a self-exploration program to produce training data indicative of a space of the original trajectory, and learn, using the training data, a non-linear compliant control law including a non-linear mapping that maps measurements of a force sensor of the robot to a direction of corrections to the original trajectory defining the control law. The processor transforms the original trajectory according to a new goal pose to produce a transformed trajectory, update the control law according to the transformed trajectory to produce the updated control law, and command the plurality of motors to control the robotic arm according to the updated control law corrected with the compliance control law.

Abstract: A system for parameter tuning for robotic manipulators is provided. The system includes an interface configured to receive a task specification, a plurality of physical parameters, and a plurality of control parameters, wherein the interface is configured to communicate with a real-world robot via a robot controller. The system further includes a memory to store computer-executable programs including a robot simulation module, a robot controller, and an auto-tuning module a processor, in connection with the memory. In this case, the processor is configured to acquire, in communication with the real-world robot, state values of the real-world robot, state values of the robot simulation module, simultaneously update, by use of a predetermined optimization algorithm with the auto-tuning module, an estimate of one or more of the physical, and said control parameters, and store the updated parameters.

Abstract: An artificial intelligence (AI) system is disclosed. The AI system includes a processor that processes a sequence of input frames with a neural network including a dilated self-attention module trained to compute a sequence of outputs by transforming each input frame into a corresponding query frame, a corresponding key frame, and a corresponding value frame leading to a sequence of key frames, a sequence of value frames, and a sequence of query frames of same ordering and by performing attention calculations for each query frame with respect to a combination of a portion of the sequences of key and value frames restricted based on a location of the query frame and a dilation sequence of the key frames and a dilation sequence of value frames extracted by processing different frames of the sequences of key and value frames with a predetermined extraction function. Further, the processor renders the sequence of outputs.

Abstract: An anomaly detector for detecting anomaly in input data comprises an auto-encoder trained to encode the input data and decode the encoded input data to reconstruct the input data. Further, the anomaly detector comprises a classifier trained to determine a reconstruction loss indicative of a difference between the accepted input data and the reconstructed input data, where the reconstruction loss includes a weighted combination of a plurality of loss functions evaluating reconstruction losses of a plurality of parts of the reconstructed input data, different types of loss functions, or both. The classifier is further configured to detect an anomaly in the reconstructed input data when the reconstruction loss is above a threshold.

Abstract: A robot configured for multi-robot pose estimation execute iteratively the primary particle filter and the secondary particle filter independently from each other to update the pose estimations in the primary and the secondary particles. In response to each encounter of the robot with a neighboring robot, the robot receives neighboring particles of a particle filter of the neighboring robot identifying poses and likelihoods of the poses of the neighboring robot and replaces the primary particles of the primary particle filter with the secondary particles fused with the neighboring particles. The robot outputs its pose according to the primary particles of the primary particle filter.

Abstract: A computer-implemented method of training an autoencoder to recover missing data is provided. The autoencoder includes an encoder for encoding its inputs into a latent space and a decoder for decoding the encodings from the latent space. The method comprises creating a first training set including a valid data set of multiple dimensions, and training the encoder and the decoder in a first training stage using the first training set to reduce a difference between the valid data set provided to the encoder and a data set decoded by the decoder. The method further comprises creating a second training set comprising an invalid data set, and training the encoder in a second training stage using the second training set to reduce a difference between encodings of valid data instances and encodings of their corresponding invalid data instances.

Abstract: An extremum seeking control system and a method for controlling a system is provided. The method comprises receiving a tracking error, consecutive measurements of a system output, and consecutive measurements of a cost function of the system, and estimating a gradient based on the tracking error, the consecutive measurements of the system output, and the consecutive measurements of the cost function of the system. The method further comprises determining a step-size based on a ratio of a covariance of the gradient to the gradient, and determining a set-point optimizing performance of the system, based on the gradient and the step-size. The method further comprises controlling the system based on the set-point optimizing performance of the system.

Abstract: A scene captioning system is provided. The scene captioning system includes an interface configured to acquire a stream of scene data signals including frames and sound data, a memory to store a computer-executable scene captioning model including a scene encoder, a timing decoder, a timing detector, and a caption decoder, wherein the audio-visual encoder is shared by the timing decoder and the timing detector and the caption decoder, and a processor, in connection with the memory. The processor is configured to perform steps of extracting scene features from the scene data signals by use of the audio-visual encoder, determining a timing of generating a caption by use of the timing detector, wherein the timing is arranged an early stage of the stream of scene data signals, and generating the caption based on the scene features by using the caption decoder according to the timing.

Abstract: A time synchronization system an input interface configured to receive pseudorange measurements from a set of antennas of at least one Global Navigation Satellite System (GNSS) receiving system that operates the antennas at known positions according to a clock with an unknown time bias to receive signals from satellites in sight of the antennas and an output interface configured to output the determined time bias. The time synchronization system also includes a processor configured to compare the pseudorange measurements of different antennas to produce an antenna-specific clock offset for each of the antennas, correct the pseudorange measurements of the antennas according to the antenna-specific clock offsets of corresponding antennas to produce corrected pseudorange measurements, and determine the time bias of the clock using the corrected pseudorange measurements.

Abstract: A wind flow sensing system for determining a turbulence of wind flow at a set of different altitudes above a terrain is provided. The wind flow sensing system comprises an input interface configured to receive a set of measurements of radial velocities at line-of-site points above the terrain for each of the altitudes for a set of time steps, and a processor configured to estimate velocity fields for each of the altitudes based on data assimilation of the velocity fields, estimate unbiased horizontal velocities at each of the altitudes and for each time step, determine an average of the unbiased horizontal velocities for a period of time including the set of time steps, and determine, at each of the altitudes, a turbulence based on the unbiased horizontal velocities for each time step and the average of the unbiased horizontal velocity.

Abstract: A system for detection of an anomaly in a discrete manufacturing process (DMP) with human-robot teams executing a task. Receive signals including robot, worker and DMP signals. Predict a sequence of events (SOEs) from DMP signals. Determine whether the predicted SOEs in the DMP signals is inconsistent with a behavior of operation of the DMP described in a DMP model, and if the predicted SOEs from DMP signals is inconsistent with the behavior, then an alarm is to be signaled. Input worker data into a Human Performance (HP) model, to obtain a state of the worker based on previously learned boundaries of human state. The state of the HW is then input into the HRI model and the DMP model to determine a classification of anomaly or no anomaly. Update a Human-Robot Interaction (HRI) model to obtain a control action of a robot or a type of an anomaly alarm.