Abstract: Microfluidic device and application method thereof are provided. The microfluidic device includes a first substrate and a second substrate that are oppositely arranged along a first direction; and a first storage box and a second storage box that are oppositely arranged along the first direction. The first direction is a thickness direction of the microfluidic device. The first storage box includes a first storage cavity and a first opening communicating with the first storage cavity, and the first substrate is fixed in the first storage cavity. The second storage box includes a second storage cavity and a second opening communicating with the second storage cavity, and the second substrate is fixed in the second storage cavity. Along the first direction, the first opening is arranged opposite to the second opening and the first storage box is nested with the second storage box.

Abstract: Provided is a microfluidic chip. The microfluidic chip includes a first substrate and a second substrate disposed opposite to each other and drive electrodes, first sensing electrodes and second sensing electrodes disposed on a side of the first substrate. A microfluidic channel is formed between the first substrate and the second substrate and configured to accommodate at least one droplet. Different drive voltage signals are applied to adjacent drive electrodes, so as to drive the at least one droplet to move. Detection signals are applied to the first sensing electrodes and the second sensing electrodes, and a position of the at least one droplet is determined according to a change in capacitance between one first sensing electrode and an electrode corresponding thereto and a change in capacitance between one second sensing electrode and an electrode corresponding thereto when the at least one droplet flows by.

Abstract: Microfluidic substrate, microfluidic device, and driving method thereof are provided. The microfluidic substrate includes a plurality of detection units arranged in an array. A detection unit of the plurality of detection units at least includes a first switch transistor, a second switch transistor, a drive electrode, and a photosensitive element. The microfluid substrate includes a base; a transistor array layer on a side of the base, first switch transistors and second switch transistors being on the transistor array layer; a photosensitive element array layer on a side of the transistor array layer away from the substrate, photosensitive elements being on the photosensitive element array layer; a first electrode layer on a side of the photosensitive element array layer away from the base; and a second electrode layer on a side of the first electrode layer away from the base.

Abstract: The present disclosure is generally related to a magnetic recording device comprising a magnetic recording head. The magnetic recording head comprises a main pole, a hot seed layer, and a spintronic device disposed between the main pole and the hot seed layer. The spintronic device comprises two field generation layers (FGLs), two spin polarization layers (SPLs), and two spin kill layers. The second SPL of the spintronic device drives the second FGL. The spintronic device further comprises one or more optional thin negative beta material layers, such as layers comprising FeCr, disposed in contact with at least one of the spin kill layers. When electric current is applied, the spin kill layers and optional negative beta material layers eliminate or reduce any spin torque between the FGLs and the SPLs.

Abstract: The present disclosure is generally related to a magnetic recording device comprising a magnetic recording head. The magnetic recording head comprises a main pole, a shield, and a spintronic device disposed between the main pole and the shield. The spintronic device comprises two field generation layers (FGLs), two spin polarization layers (SPLs), and two spin kill layers. The spintronic device further comprises one or more optional thin negative beta material layers, such as layers comprising FeCr, disposed in contact with at least one of the spin kill layers. When electric current is applied, the spin kill layers and optional negative beta material layers eliminate or reduce any spin torque between the FGLs and the SPLs.

Abstract: The present disclosure is generally related to a magnetic recording device comprising a magnetic recording head having a first current flow in a cross-track direction through a trailing shield. In one or more embodiments, a second current flows in a cross-track direction around the main pole. The magnetic recording device comprises a main pole disposed between a trailing shield, a leading shield, and side shields. A trailing gap is disposed between the side shields and the trailing shield. A high moment seed layer is disposed between the main pole and the trailing shield. A first insulation layer is disposed within the trailing shield and directs the first current through the trailing shield, guided to the proximity of the main pole. A second insulation layer, disposed below the trailing shield, directs the second current through the trailing shield, or alternatively through the side shields and around the main pole.

Abstract: Aspects of the present disclosure generally relate to a magnetic recording head assembly that includes an external alternating current (AC) source. A magnetic recording head of the magnetic recording head assembly includes a conductive structure between a main pole and a trailing shield. The conductive structure includes a conductive layer, and the conductive layer is nonmagnetic. The magnetic recording head assembly also includes an external AC source to supply AC current that flows through the conductive structure. In one aspect, the conductive structure is between a coil structure and the trailing shield, and the external AC source is coupled to the coil structure. The conductive structure and the external AC source facilitate consistently providing an enhanced AC writing field to facilitate effective and reliable writing, high ADC, high SNR, and reduced jitter.

Abstract: Active utilization of a robotic simulator in control of one or more real world robots. A simulated environment of the robotic simulator can be configured to reflect a real world environment in which a real robot is currently disposed, or will be disposed. The robotic simulator can then be used to determine a sequence of robotic actions for use by the real world robot(s) in performing at least part of a robotic task. The sequence of robotic actions can be applied, to a simulated robot of the robotic simulator, to generate a sequence of anticipated simulated state data instances. The real robot can be controlled to implement the sequence of robotic actions. The implementation of one or more of the robotic actions can be contingent on a real state data instance having at least a threshold degree of similarity to a corresponding one of the anticipated simulated state data instances.

Abstract: Provided is a microfluidic device. The microfluidic device includes a first substrate and a second substrate disposed opposite to each other. A cavity is formed between the first substrate and the second substrate and configured to accommodate liquid. The first substrate includes multiple drive electrodes and multiple first electrodes, and the drive electrodes are disposed on a side of the first electrodes facing the second substrate. At least one of the drive electrodes includes at least one opening, and the at least one opening, along a direction perpendicular to a plane where the first substrate is located, penetrates the drive electrode where the at least one opening is located. An orthographic projection of at least one first electrode on the plane where the first substrate is located covers at least an orthographic projection of one opening on the plane where the first substrate is located.

Abstract: Provided a microfluidic pixel driving circuit includes n boost modules, where each boost module includes a capacitor and a write unit, and n is a positive integer greater than or equal to 2; a first terminal of a first capacitor is electrically connected to a fixed potential line, a second terminal of the first capacitor is electrically connected to a pixel electrode, and the first capacitor is used for storing a voltage of the pixel electrode; a first write unit is configured to write a first data signal to the pixel electrode according to an enable level of a first scan signal; a first terminal of a second capacitor is electrically connected to the pixel electrode; and a second write unit is configured to write a second data signal to a second terminal of the second capacitor according to an enable level of a second scan signal.

Abstract: Training a machine learning model (e.g., a neural network model such as a convolutional neural network (CNN) model) so that, when trained, the model can be utilized in processing vision data (e.g., from a vision component of a robot), that captures an object, to generate a rich object-centric embedding for the vision data. The generated embedding can enable differentiation of even subtle variations of attributes of the object captured by the vision data.

Abstract: A driving circuit, a method for driving the same, and a microfluidic device are provided. The driving circuit includes a constant voltage writing module configured to transmit a constant voltage to an output terminal of the driving circuit, an AC voltage writing module configured to transmit an AC voltage to the output terminal of the driving circuit, a first switch, and a first capacitor. The first switch includes an input terminal electrically connected to a third signal line, an output terminal electrically connected to control terminals of the AC voltage writing module and the constant voltage writing module, and a control terminal electrically connected to a first scan line. The first capacitor is configured to stabilize a potential of the output terminal the first switch.

Abstract: Utilization of past dynamics sample(s), that reflect past contact physics information, in training and/or utilizing a neural network model. The neural network model represents a learned value function (e.g., a Q-value function) and that, when trained, can be used in selecting a sequence of robotic actions to implement in robotic manipulation (e.g., pushing) of an object by a robot. In various implementations, a past dynamics sample for an episode of robotic manipulation can include at least two past images from the episode, as well as one or more past force sensor readings that temporally correspond to the past images from the episode.

Abstract: Implementations described herein relate to training and refining robotic control policies using imitation learning techniques. A robotic control policy can be initially trained based on human demonstrations of various robotic tasks. Further, the robotic control policy can be refined based on human interventions while a robot is performing a robotic task. In some implementations, the robotic control policy may determine whether the robot will fail in performance of the robotic task, and prompt a human to intervene in performance of the robotic task. In additional or alternative implementations, a representation of the sequence of actions can be visually rendered for presentation to the human can proactively intervene in performance of the robotic task.

Abstract: Implementations are directed to generating simulated training examples for training of a machine learning model, training the machine learning model based at least in part on the simulated training examples, and/or using the trained machine learning model in control of at least one real-world physical robot. Implementations are additionally or alternatively directed to performing one or more iterations of quantifying a “reality gap” for a robotic simulator and adapting parameter(s) for the robotic simulator based on the determined reality gap. The robotic simulator with the adapted parameter(s) can further be utilized to generate simulated training examples when the reality gap of one or more iterations satisfies one or more criteria.

Abstract: Mitigating the reality gap through training and utilization of at least one difference model. The difference model can be utilized to generate, for each of a plurality of instances of simulated state data generated by a robotic simulator, a corresponding instance of modified simulated state data. The difference model is trained so that a generated modified instance of simulated state data is closer to “real world data” than is a corresponding initial instance of simulated state data. Accordingly, the difference model can be utilized to mitigate the reality gap through modification of initially generated simulated state data, to make it more accurately reflect what would occur in a real environment. Moreover, the difference representation from the difference model can be used as input to the control policy to adapt the control learned from simulator to the real environment.

Abstract: Implementations described herein relate to training and refining robotic control policies using imitation learning techniques. A robotic control policy can be initially trained based on human demonstrations of various robotic tasks. Further, the robotic control policy can be refined based on human interventions while a robot is performing a robotic task. In some implementations, the robotic control policy may determine whether the robot will fail in performance of the robotic task, and prompt a human to intervene in performance of the robotic task. In additional or alternative implementations, a representation of the sequence of actions can be visually rendered for presentation to the human can proactively intervene in performance of the robotic task.

Abstract: Generating edge-depth values for an object, utilizing the edge-depth values in generating a 3D point cloud for the object, and utilizing the generated 3D point cloud for generating a 3D bounding shape (e.g., 3D bounding box) for the object. Edge-depth values for an object are depth values that are determined from frame(s) of vision data (e.g., left/right images) that captures the object, and that are determined to correspond to an edge of the object (an edge from the perspective of frame(s) of vision data). Techniques that utilize edge-depth values for an object (exclusively, or in combination with other depth values for the object) in generating 3D bounding shapes can enable accurate 3D bounding shapes to be generated for partially or fully transparent objects. Such increased accuracy 3D bounding shapes directly improve performance of a robot that utilizes the 3D bounding shapes in performing various tasks.

Abstract: A microfluidic apparatus, a driving method, and a formation method are provided in the present disclosure. The apparatus includes a first substrate and a second substrate. The first substrate and the second substrate are both smooth substrates. An electrode array layer is on a side of the first substrate; and a second electrode layer is on a side of the second substrate. The electrode array layer at least includes a plurality of first electrodes and a plurality of second electrodes. The first substrate includes a first region and a second region; the plurality of first electrodes is in the first region; and the plurality of second electrode is in the second region. A distance between the first substrate and the second substrate in the first region is D1 is greater than a distance between the first substrate and the second substrate in the second region is D2.

Abstract: Mitigating the reality gap through optimization of one or more simulated hardware parameters for simulated hardware components of a simulated robot. Implementations generate and store real navigation data instances that are each based on a corresponding episode of locomotion of a real robot. A real navigation data instance can include a sequence of velocity control instances generated to control a real robot during a real episode of locomotion of the real robot, and one or more ground truth values, where each of the ground truth values is a measured value of a corresponding property of the real robot (e.g., pose). The velocity control instances can be applied to a simulated robot, and one or more losses can be generated based on comparing the ground truth value(s) to corresponding simulated value(s) generated from applying the velocity control instances to the simulated robot. The simulated hardware parameters and environmental parameters can be optimized based on the loss(es).