Abstract: A multi-degree-of-freedom continuum robot with a flexible target grasping function comprises a driving device module, a trunk simulation module and a nimble finger module. The trunk simulation module is composed of a rotary compression module and a bending compression module. Each module has a unified connection interface reserved at the end, and is combined and assembled according to actual needs. The driving module is arranged on the base of the robot to realize the driving operation of all cables to control the motion of the robot. The rotary compression module can simultaneously generate the motion in the forms of rotation and compression, thereby compensating for the defect of blind angle of the bending compression module. The bending compression module can realize compression deformation and bending deformation of the module independently. The nimble finger module realizes a grasping function by multi-finger collaboration.

Abstract: An example apparatus having a heterogenous memory system includes a first sensor layer, of a plurality of stacked sensor layers, including an array of pixels; and one or more semiconductor layers of the plurality of stacked sensor layers located beneath the first sensor layer, the one or more semiconductor layers configured to process pixel data output by the array of pixels, the one or more semiconductor layers including a first memory to store most significant bits (“MSBs”) of data involved in the processing of the pixel data; a second memory to store least significant bits (“LSBs”) of the data; and wherein the first memory has a lower bit error rate (“BER”) than the second memory.

Abstract: The disclosed computer-implemented method may include (1) receiving a present frame of a video stream, the present frame comprising a present depiction of a multi-segment articulated body system, (2) identifying a previous frame of the video stream that comprises a previous depiction of the multi-segment articulated body system, (3) analyzing the present frame and the previous frame to determine whether the multi-segment articulated body system remained substantially rigid between the previous frame and the present frame, and (4) estimating a pose of the multi-segment articulated body system in the present frame using a first pose estimation computation that treats the multi-segment articulated body system as rigid and that is selected in contrast to a second pose estimation computation based on determining that the multi-segment articulated body system remained substantially rigid between the previous frame and the present frame. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A console and headset system locally trains machine learning models to perform customized online learning tasks. To customize the online learning models for specific users of the system without using outside resources, the system trains the models to compare a target frame to stored calibration frames, rather than directly inferring information about a target frame. During deployment, an embedding is generated for the target frame. A sample embedding that is closest to the target embedding is selected from a group of embeddings of calibration frames. The information about the selected embedding and target embedding and ground truths for the calibration frame are provided as inputs to one of the trained models. The model predicts a difference between the target frame and the calibration frame, which can be used to determine information about the target frame.

Abstract: Methods, systems, and media for partitioning neural networks are provided. In some embodiments, a method comprises obtaining a training set. The method comprises training a plurality of neural networks using the training set, wherein neural networks differ based on dimensions of one or more layers of the neural networks and a location of a compression block positioned between a first set of layers of a neural network and a second set of layers of the neural network. The method comprises selecting a neural network based on hardware constraints of a system on which the neural network is to be implemented, wherein the first set of layers of the selected neural network are executed by one or more sensor devices of the system, and wherein the second set of layers of the selected neural network are executed by an aggregator computing device of the system.

Abstract: The disclosed system may include a first layer that includes multiple digital pixel sensors configured to detect light. The system may also include a second layer that includes various image processing components configured to process the light detected by the digital pixel sensors. Still further, the system may include a third layer that includes machine learning (ML) hardware processing components. The image processing components of the second layer may be communicatively connected to the ML hardware processing components of the third layer via multiple micro through-silicon vias (uTSVs). Various other methods of manufacturing, apparatuses, and computer-readable media are also disclosed.

Abstract: In one example, an apparatus comprises: a first sensor layer, including an array of pixel cells configured to generate pixel data; and one or more semiconductor layers located beneath the first sensor layer with the one or more semiconductor layers being electrically connected to the first sensor layer via interconnects. The one or more semiconductor layers comprises on-chip compute circuits configured to receive the pixel data via the interconnects and process the pixel data, the on-chip compute circuits comprising: a machine learning (ML) model accelerator configured to implement a convolutional neural network (CNN) model to process the pixel data; a first memory to store coefficients of the CNN model and instruction codes; a second memory to store the pixel data of a frame; and a controller configured to execute the codes to control operations of the ML model accelerator, the first memory, and the second memory.

Abstract: A stacked camera-image-sensor circuit may include (i) a first layer that includes a plurality of image sensing elements, (ii) a second layer that includes components that interface with the image sensing elements, and (iii) at least one additional layer that includes image-processing components. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: In one example, an apparatus comprises: a memory to store input data and weights, the input data comprising groups of data elements, each group being associated with a channel of channels, the weights comprising weight tensors, each weight tensor being associated with a channel of the channels; a data sparsity map generation circuit configured to generate, based on the input data, a channel sparsity map and a spatial sparsity map, the channel sparsity map indicating channels associated with first weights tensors to be selected, the spatial sparsity map indicating spatial locations of first data elements; a gating circuit configured to: fetch, based on the channel sparsity map and the sparsity map, the first weights tensors and the first data elements from the memory; and a processing circuit configured to perform neural network computations on the first data elements and the first weights tensors to generate a processing result.

Abstract: A sensor assembly for determining one or more features of a local area is presented herein. The sensor assembly includes a plurality of stacked sensor layers. A first sensor layer of the plurality of stacked sensor layers located on top of the sensor assembly includes an array of pixels. The top sensor layer can be configured to capture one or more images of light reflected from one or more objects in the local area. The sensor assembly further includes one or more sensor layers located beneath the top sensor layer. The one or more sensor layers can be configured to process data related to the captured one or more images. Different sensor architectures featuring various arrangements of memory and computing devices are described, some of which feature in-memory computing. A plurality of sensor assemblies can be integrated into an artificial reality system, e.g., a head-mounted display.

Abstract: Optical flow is measured between a first image and a second image by evaluating a match quantifying parameter in respect of a set of candidate flow vectors. The set of candidate flow vectors includes one or more flow vectors selected in dependence upon one or more neighbor flow vectors associated with one or more neighboring pixels to the given pixel which have previously calculated respective match quantifying parameters indicative of closest matches for the one or more neighboring pixels. The set of candidate flow vectors also includes adjacent flow vectors corresponding to target pixels surrounding the target pixels identified by the neighbor flow vectors. One or more randomly selected random flow vectors is also added to the set of candidate flow vectors. The calculated match quantifying parameters are weighted in dependence upon whether the corresponding candidate flow vector is similar to any other candidate flow vector.

Abstract: The present disclosure provides a diesel fuel composition containing DMC and multifunctional additives to reduce particulate emission, improve efficiency and be used in cold and/or hypoxia conditions.

Abstract: Optical flow is measured between a first image and a second image by evaluating a match quantifying parameter in respect of a set of candidate flow vectors. The set of candidate flow vectors includes one or more flow vectors selected in dependence upon one or more neighbor flow vectors associated with one or more neighboring pixels to the given pixel which have previously calculated respective match quantifying parameters indicative of closest matches for the one or more neighboring pixels. The set of candidate flow vectors also includes adjacent flow vectors corresponding to target pixels surrounding the target pixels identified by the neighbor flow vectors. One or more randomly selected random flow vectors is also added to the set of candidate flow vectors.

Abstract: The present disclosure provides a diesel fuel composition containing DMC and multifunctional additives to reduce particulate emission, improve efficiency and be used in cold and/or hypoxia conditions.