Abstract: Methods and systems for predicting a trajectory an autonomous vehicle (AV) are disclosed. A method includes generating, based on sensor data from a sensing system of the AV, one or more embeddings, generating, using a machine learning model (MLM) and the one or more embeddings, one or more predicted future trajectories for the AV, and causing, using the one or more predicted future trajectories, a planning system of the AV to generate an update to a current trajectory of the AV.

Abstract: A telescoping seating system for a venue includes a telescoping riser including a plurality of decks that are extendable and retractable relative to one another. A plurality of mounting brackets are disposed at the plurality of decks. A seating assembly is configured to be removably attached to a pair of adjacent mounting brackets of the plurality of mounting brackets to mount the seating assembly at one deck of the plurality of decks of the riser. The seating assembly includes (i) a seat and (ii) a pair of stanchions. With the seating assembly mounted at the riser, each stanchion of the pair of stanchions is received by a respective mounting bracket of the pair of adjacent mounting brackets. With the seating assembly removed from the riser, the seating assembly is configured to mount to a stillage for storage of the seating assembly.

Abstract: Implementations disclosed herein relate to mitigating the reality gap through feature-level domain adaptation in training of a vision-based robotic action machine learning (ML) model. Implementations mitigate the reality gap through utilization of embedding consistency losses and/or action consistency losses during training of the action ML model.

Abstract: The disclosed systems and techniques facilitate efficient detection and navigation of reduced drivability areas in driving environments. The disclosed techniques include, obtaining, using a sensing system of a vehicle, a set of camera images, a set of radar images, and/or a set of lidar images of an environment. The techniques further include generating, using a first neural network (NN), camera feature(s) characterizing the camera images, generating, using a second NN, radar features characterizing the radar images, and/or generating, using a third NN, lidar feature(s) characterizing the lidar images. The techniques further include processing the camera feature(s), the radar feature(s), and the lidar feature(s) to obtain an indication of a reduced drivability area in the environment.

Abstract: Methods and system are provided for dynamic process control in substrate processing, for example in semiconductor manufacturing applications. Some example systems and methods are provided for advanced monitoring and machine learning in atomic layer deposition (ALD) processes. Some examples also relate to dynamic process control and monitoring for chamber parameter matching and gas line charge times.

Abstract: The disclosed systems and techniques facilitate efficient detection and classification of traffic signs in driving environments. The disclosed techniques include, obtaining, using a sensing system of a vehicle a first set of perspective camera images of an environment and a second set of radar images of the environment. The techniques further include generating, using a first neural network, one or more camera features characterizing the first set of images, generating, using a second neural network, one or more radar features characterizing the second set of images, and processing the one or more camera features and the one or more radar features to obtain an identification of one or more traffic signs in the environment.

Abstract: Implementations disclosed herein relate to mitigating the reality gap through feature-level domain adaptation in training of a vision-based robotic action machine learning (ML) model. Implementations mitigate the reality gap through utilization of embedding consistency losses and/or action consistency losses during training of the action ML model.

Abstract: Methods and system are provided for dynamic process control in substrate processing, for example in semiconductor manufacturing applications. Some example systems and methods are provided for advanced monitoring and machine learning in atomic layer deposition (ALD) processes. Some examples also relate to dynamic process control and monitoring for chamber parameter matching and gas line charge times.

Abstract: Methods and systems for predicting a trajectory an autonomous vehicle (AV) are disclosed. A method includes generating, based on sensor data from a sensing system of the AV, one or more embeddings, generating, using a machine learning model (MLM) and the one or more embeddings, one or more predicted future trajectories for the AV, and causing, using the one or more predicted future trajectories, a planning system of the AV to generate an update to a current trajectory of the AV.

Abstract: A chair seat has a frame having a groove on the outer side wall of the frame and substantially extending around the frame, a cover having a drawstring incorporated in a hem which substantially extends around the cover, the hem and drawstring being disposed in the groove, and at least one edging strip disposed in the groove which constrains the hem and drawstring of the mesh cover and holds it taut.

Abstract: Some implementations relate to using trained robotic action ML models in controlling a robot to perform a robotic task. Some versions of those implementations include (a) a first modality robotic action ML model that is used to generate, based on processing first modality sensor data instances, first predicted action outputs for the robotic task and (b) a second modality robotic action ML model that is used to generate, in parallel and based on processing second modality sensor data instances, second predicted action outputs for the robotic task. In some of those versions, respective weights for each pair of the first and second predicted action outputs are dynamically determined based on analysis of embeddings generated in generating the first and second predicted action outputs. A final predicted action output, for controlling the robot, is determined based on the weights.

Abstract: A chair comprising a seat base, a back rest, the back rest having a first arm connected to the seat base by a first pivot and a second arm connected to the seat base by a second pivot so that the back rest can pivot between a first folded position and a second position, the first pivot being biased so that in the absence of any force applied by a user, the back rest maintains the first position wherein a damper is included to ensure to retard the movement of the seat from the second position to the first position in the absence of any force applied.

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 disclosed herein relate to mitigating the reality gap through feature-level domain adaptation in training of a vision-based robotic action machine learning (ML) model. Implementations mitigate the reality gap through utilization of embedding consistency losses and/or action consistency losses during training of the action ML model.

Abstract: A mechanism for raising and lowering an armrest for a seat includes an armrest, a rotatable hub, and an actuating linkage between the armrest and the hub, the rotatable hub having a cam that operates the actuating linkage to raise the armrest wherein the cam can rotate through a first angle of rotation where the actuating linkage is not operated a second angle of rotation where the actuating linkage is operated. The cam can rotate through a third angle of rotation where the actuating linkage is not operated, this angle being different to the first angle of rotation. The cam is torsionally linked to the rotation of a seat member.

Abstract: A method for managing a current revision on prior revision of an order is described. A modified set of compensations tasks for addressing amendments indicated in the current revision are determined. The modified set of compensation tasks may be determined by carrying over one or more compensation tasks, associated with the prior revision, that have not yet been completed. Alternatively or additionally, the modified set of compensation tasks may be determined by adding new compensation tasks based on the current revision.

Abstract: Methods and system are provided for dynamic process control in substrate processing, for example in semiconductor manufacturing applications. Some example systems and methods are provided for advanced monitoring and machine learning in atomic layer deposition (ALD) processes. Some examples also relate to dynamic process control and monitoring for chamber parameter matching and gas line charge times.

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: A method for managing a current revision on prior revision of an order is described. A modified set of compensations tasks for addressing amendments indicated in the current revision are determined. The modified set of compensation tasks may be determined by carrying over one or more compensation tasks, associated with the prior revision, that have not yet been completed. Alternatively or additionally, the modified set of compensation tasks may be determined by adding new compensation tasks based on the current revision.

Abstract: A method for managing a current revision on prior revision of an order is described. A modified set of compensations tasks for addressing amendments indicated in the current revision are determined. The modified set of compensation tasks may be determined by carrying over one or more compensation tasks, associated with the prior revision, that have not yet been completed. Alternatively or additionally, the modified set of compensation tasks may be determined by adding new compensation tasks based on the current revision.