Abstract: Systems and methods for controlling the motion of an autonomous are provided. In one example embodiment, a computer-implemented method includes obtaining data associated with an object within a surrounding environment of an autonomous vehicle. The data associated with the object is indicative of a predicted motion trajectory of the object. The method includes determining a vehicle action sequence based at least in part on the predicted motion trajectory of the object. The vehicle action sequence is indicative of a plurality of vehicle actions for the autonomous vehicle at a plurality of respective time steps associated with the predicted motion trajectory. The method includes determining a motion plan for the autonomous vehicle based at least in part on the vehicle action sequence. The method includes causing the autonomous vehicle to initiate motion control in accordance with at least a portion of the motion plan.

Abstract: A method of operating a laundry treating appliance having a tub, a rotatable drum at least partially defining a treating chamber, and a treating chemistry dispenser comprising multiple treating chemistry cups configured to dispense treating chemistry to the treating chamber. The method comprises performing a first wash cycle and dispensing a first dose of treating chemistry from one of the treating chemistry cups, receiving at a controller a user selection of an activation of a performance enhancement selector, and upon activation of the performance enhancement selector, activating a second wash cycle comprising dispensing a second dose of treating chemistry from another of treating chemistry cups.

Abstract: The present disclosure provides autonomous vehicle systems and methods that include or otherwise leverage a motion planning system that generates constraints as part of determining a motion plan for an autonomous vehicle (AV). In particular, a scenario generator within a motion planning system can generate constraints based on where objects of interest are predicted to be relative to an autonomous vehicle. A constraint solver can identify navigation decisions for each of the constraints that provide a consistent solution across all constraints. The solution provided by the constraint solver can be in the form of a trajectory path determined relative to constraint areas for all objects of interest. The trajectory path represents a set of navigation decisions such that a navigation decision relative to one constraint doesn't sacrifice an ability to satisfy a different navigation decision relative to one or more other constraints.

Abstract: An automatic speech sensitivity adjustment feature is provided. The described sensitivity feature can enable an automatic system adjustment of a sensitivity level based on the number and type of determined speech errors. The sensitivity level determines how sensitive the sensitivity feature will be when indicating speech errors. The sensitivity feature can receive audio input comprising one or more spoken words and determine speech errors for the audio input using at least a sensitivity level. The sensitivity feature can determine whether an amount and type of the speech errors requires an adjustment to the sensitivity level. The sensitivity feature can adjust the sensitivity level to a second sensitivity level based on the amount and type of the speech errors, where the second sensitivity level is a different level than the sensitivity level. The sensitivity feature can re-determine the speech errors for the audio input using at least the second sensitivity level.

Abstract: A method of generating an output trajectory of an ego vehicle includes recording trajectory data of the ego vehicle and pedestrian agents from a scene of a training environment of the ego vehicle. The method includes identifying at least one pedestrian agent from the pedestrian agents within the scene of the training environment of the ego vehicle causing a prediction-discrepancy by the ego vehicle greater than the pedestrian agents within the scene. The method includes updating parameters of a motion prediction model of the ego vehicle based on a magnitude of the prediction-discrepancy caused by the at least one pedestrian agent on the ego vehicle to form a trained, control-aware prediction objective model. The method includes selecting a vehicle control action of the ego vehicle in response to a predicted motion from the trained, control-aware prediction objective model regarding detected pedestrian agents within a traffic environment of the ego vehicle.

Abstract: Systems and methods for vehicle spatial path sampling are provided. The method includes obtaining an initial travel path for an autonomous vehicle from a first location to a second location and vehicle configuration data indicative of one or more physical constraints of the autonomous vehicle. The method includes determining one or more secondary travel paths for the autonomous vehicle from the first location to the second location based on the initial travel path and the vehicle configuration data. The method includes generating a spatial envelope based on the one or more secondary travel paths that indicates a plurality of lateral offsets from the initial travel path. And, the method includes generating a plurality of trajectories for the autonomous vehicle to travel from the first location to the second location such that each of the plurality of trajectories include one or more lateral offsets identified by the spatial envelope.

Abstract: A laundry treating appliance having a treating chamber and a bulk dispenser and a user interface configured to provide an indication to the user related to the bulk chemistry dispenser and a controller coupled with the user interface and the bulk treating chemistry dispenser and configured to execute an operating cycle including controlling the treating chemistry meter to dispense the predetermined amount of treating chemistry and determine a number of doses of treating chemistry available in the bulk treating chemistry dispenser and method of determining the number of doses and the types of a treating chemistry available in the bulk dispensing system, and providing an indication of the determination on a user interface.

Abstract: A method for sequential point cloud forecasting is described. The method includes training a vector-quantized conditional variational autoencoder (VQ-CVAE) framework to map an output to a closest vector in a discrete latent space to obtain a future latent space. The method also includes outputting, by a trained VQ-CVAE, a categorical distribution of a probability of V vectors in a discrete latent space in response to an input previously sampled latent space and past point cloud sequences. The method further includes sampling an inferred future latent space from the categorical distribution of the probability of the V vectors in the discrete latent space. The method also includes predicting a future point cloud sequence according to the inferred future latent space and the past point cloud sequences. The method further includes denoising, by a denoising diffusion probabilistic model (DDPM), the predicted future point cloud sequences according to an added noise.

Abstract: The present disclosure is directed to generating vehicle motion corridors for use in generating autonomous vehicle paths. In particular, a computing system can access map data for a geographic area and sensor data for the geographic area around an autonomous vehicle. The computing system can identify, based on the map data and the sensor data, object data describing a position and a size of one or more objects in the geographic area of the autonomous vehicle. The computing system can access path data describing a nominal path through the geographic area. The computing system can determine a plurality of corridor segments associated with the nominal path. The computing system can generate a vehicle motion corridor by aggregating the plurality of corridor segments, wherein the vehicle motion corridor defines an area in which the autonomous vehicle can travel without colliding with the one or more objects.

Abstract: Systems and methods for basis path generation are provided. In particular, a computing system can obtain a target nominal path. The computing system can determine a current pose for an autonomous vehicle. The computing system can determine, based at least in part on the current pose of the autonomous vehicle and the target nominal path, a lane change region. The computing system can determine one or more merge points on the target nominal path. The computing system can, for each respective merge point in the one or more merge points, generate a candidate basis path from the current pose of the autonomous vehicle to the respective merge point. The computing system can generate a suitability classification for each candidate basis path. The computing system can select one or more candidate basis paths based on the suitability classification for each respective candidate basis path in the plurality of candidate basis paths.

Abstract: The disclosure provides, e.g., compositions, systems, and methods for targeting, editing, modifying, or manipulating a host cell's genome at one or more locations in a DNA sequence in a cell, tissue, or subject. Gene modifying systems for treating sickle cell disease (SCD) are described.

Abstract: The present disclosure is directed to altering vehicle paths. In particular, a computing system can access map data for a geographic area. The computing system can obtain target zone data describing a target zone within the geographic area. The computing system can determine an altered nominal path to traverse the target zone. The computing system can designate a portion of the altered nominal path as a designated action region associated with the target zone. The computing system can generate a longitudinal plan for an autonomous vehicle through the geographic area based on the altered nominal path. The computing system can generate a target velocity for one or more portions of the nominal path within the designated action region. The computing system can generate a trajectory for the autonomous vehicle based on the target velocity and the altered nominal path.

Abstract: Systems and methods for controlling the motion of an autonomous are provided. In one example embodiment, a computer-implemented method includes obtaining data associated with an object within a surrounding environment of an autonomous vehicle. The data associated with the object is indicative of a predicted motion trajectory of the object. The method includes determining a vehicle action sequence based at least in part on the predicted motion trajectory of the object. The vehicle action sequence is indicative of a plurality of vehicle actions for the autonomous vehicle at a plurality of respective time steps associated with the predicted motion trajectory. The method includes determining a motion plan for the autonomous vehicle based at least in part on the vehicle action sequence. The method includes causing the autonomous vehicle to initiate motion control in accordance with at least a portion of the motion plan.

Abstract: Systems and methods for vehicle spatial path sampling are provided. The method includes obtaining an initial travel path for an autonomous vehicle from a first location to a second location and vehicle configuration data indicative of one or more physical constraints of the autonomous vehicle. The method includes determining one or more secondary travel paths for the autonomous vehicle from the first location to the second location based on the initial travel path and the vehicle configuration data. The method includes generating a spatial envelope based on the one or more secondary travel paths that indicates a plurality of lateral offsets from the initial travel path. And, the method includes generating a plurality of trajectories for the autonomous vehicle to travel from the first location to the second location such that each of the plurality of trajectories include one or more lateral offsets identified by the spatial envelope.

Abstract: The present disclosure provides autonomous vehicle systems and methods that include or otherwise leverage a motion planning system that generates constraints as part of determining a motion plan for an autonomous vehicle (AV). In particular, a scenario generator within a motion planning system can generate constraints based on where objects of interest are predicted to be relative to an autonomous vehicle. A constraint solver can identify navigation decisions for each of the constraints that provide a consistent solution across all constraints. The solution provided by the constraint solver can be in the form of a trajectory path determined relative to constraint areas for all objects of interest. The trajectory path represents a set of navigation decisions such that a navigation decision relative to one constraint doesn't sacrifice an ability to satisfy a different navigation decision relative to one or more other constraints.

Abstract: Systems and methods for basis path generation are provided. In particular, a computing system can obtain a target nominal path. The computing system can determine a current pose for an autonomous vehicle. The computing system can determine, based at least in part on the current pose of the autonomous vehicle and the target nominal path, a lane change region. The computing system can determine one or more merge points on the target nominal path. The computing system can, for each respective merge point in the one or more merge points, generate a candidate basis path from the current pose of the autonomous vehicle to the respective merge point. The computing system can generate a suitability classification for each candidate basis path. The computing system can select one or more candidate basis paths based on the suitability classification for each respective candidate basis path in the plurality of candidate basis paths.

Abstract: The present disclosure provides autonomous vehicle systems and methods that include or otherwise leverage a machine-learned yield model. In particular, the machine-learned yield model can be trained or otherwise configured to receive and process feature data descriptive of objects perceived by the autonomous vehicle and/or the surrounding environment and, in response to receipt of the feature data, provide yield decisions for the autonomous vehicle relative to the objects. For example, a yield decision for a first object can describe a yield behavior for the autonomous vehicle relative to the first object (e.g., yield to the first object or do not yield to the first object). Example objects include traffic signals, additional vehicles, or other objects. The motion of the autonomous vehicle can be controlled in accordance with the yield decisions provided by the machine-learned yield model.

Abstract: The disclosure provides, e.g., compositions, systems, and methods for targeting, editing, modifying, or manipulating a host cell's genome at one or more locations in a DNA sequence in a cell, tissue, or subject. Gene modifying systems for treating cystic fibrosis, e.g., in subjects having a mutation resulting in F508del, are described.

Abstract: The disclosure provides, e.g., compositions, systems, and methods for targeting, editing, modifying, or manipulating a host cell's genome at one or more locations in a DNA sequence in a cell, tissue, or subject. Gene modifying systems for treating alpha-1 antitrypsin deficiency (AATD) are described.

Abstract: The disclosure provides, e.g., compositions, systems, and methods for targeting, editing, modifying, or manipulating a host cell's genome at one or more locations in a DNA sequence in a cell, tissue, or subject. Gene modifying systems for treating phenylketonuria (PKU) are described.