Patent Publication Number: US-11640517-B2

Title: Update of local features model based on correction to robot action

Description:
BACKGROUND 
     A robot that has been purchased and put into use may be able to perform a variety of actions. However, in some situations the robot may still perform some actions incorrectly and/or be unable to perform some actions. Such occurrences may be due to a variety of factors such as lack of accuracy and/or robustness of model(s) utilized by the robot in performing actions, varied and/or dynamic environments in which the robot operates, etc. Moreover, for many occurrences of incorrect performance of an action, the robot itself may be unable to recognize the incorrect performance. For example, control processes of the robot may deem an action as correctly performed, despite the performance being incorrect in actuality. 
     SUMMARY 
     The present disclosure is directed to methods, apparatus, and computer-readable media (transitory and non-transitory) for determining and utilizing human corrections to robot actions. Some of those implementations are directed to updating a features model of a robot in response to determining a human correction of an action performed by the robot. The features model can be a “local” features model, in that it is utilized by only the robot and/or in that it is stored locally at the robot. The features model is used to determine, based on an embedding generated over a corresponding neural network model, one or more features that are most similar to the generated embedding. For example, the features model may include stored mappings between features and corresponding feature embeddings. A feature can be determined to be most similar to the generated embedding based on the feature being mapped, in the features model, to a feature embedding that is most similar to the generated embedding. 
     In such an example, the robot action can be performed based on the feature, and the human correction can indicate that the feature is incorrect and/or that an additional feature is instead correct (or is also correct). In response to the human correction, the features model can be updated. For example, an additional feature embedding that is mapped to the additional feature can be adjusted (i.e., to make it more similar to the generated embedding) and/or the feature embedding that is mapped to the feature in the model can be adjusted (i.e., to make it less similar to the generated embedding). As another example, if the additional feature is not included in the model, the additional feature can be included and assigned a feature embedding that is based on (e.g., the same as) the generated embedding. The features model can be updated in response to human corrections, and can be updated without requiring retraining of the corresponding neural network model. 
     As one particular example of implementations disclosed herein, the features model can be a classification model. The classification model can be locally stored on a robot and can map classifications to corresponding classification embeddings. For example, a “bowl” classification can be mapped to Classification Embedding A, a “cup” classification can be mapped to Classification Embedding B, a “hat” classification can be mapped to Classification Embedding C, etc. Each classification embedding can be, for example, a vector of values that defines a position in a multidimensional common embedding space. 
     A neural network model that corresponds to the classification model can also be locally stored on the robot. The neural network model can be trained to generate embeddings of sensor data instances, and can be trained in cooperation with the classification model. For example, vision sensor data generated by a vision sensor of the robot (e.g., a monocular camera, stereographic camera, laser scanner) can be applied as input to the neural network model, and an embedding can be generated over the neural network model based on the applied input. The generated embedding can then be compared to one or more of the classification embeddings to determine a classification embedding that is most similar to the generated embedding. For example, where the generated embedding and the classification embeddings are each vectors of corresponding values, distance measures between the generated embedding and the classification embeddings can be generated, and the smallest distance measure can indicate the most similar classification embedding. 
     The classification corresponding to the most similar classification embedding can then be determined as the classification of an object captured by the vision sensor data. For example, the “bowl” classification can be determined as the classification of an object captured by the vision sensor data (i.e., based on the generated embedding being determined to be most similar to Classification Embedding A that is mapped to the “bowl” classification). The robot can then perform one or more actions directed toward the object. For example, the robot can pick up the object and provide audible output of “I&#39;m picking up a bowl”. 
     During or after performance of the action, user interface input can be received that indicates the classification is incorrect (e.g., spoken input of “that&#39;s not a bowl”) and/or that indicates an alternative classification is instead the correct classification (e.g., spoken input of “that&#39;s a hat”). In response to the human correction, the locally stored classification model can be updated. For example, based on user interface input that indicates “hat” is the correct classification, Classification Embedding C (that is mapped to the “hat” classification) can be adjusted based on the generated embedding. For instance, Classification Embedding C can be adjusted to be more similar to the generated embedding. As one particular instance, the current Classification Embedding C and the generated embedding can be averaged, and the average utilized as a New Classification Embedding for the “hat” classification (e.g., the new Classification Embedding can supplant Classification Embedding C as the mapped classification embedding for the “hat” classification). As another example, based on the user interface input that indicates “bowl” is the incorrect classification, Classification Embedding A (that is mapped to the “bowl” classification) can be adjusted based on the generated embedding. For instance, Classification Embedding A can be adjusted to be less similar to the generated embedding. 
     It is noted that such adjustment(s) to the classification model can immediately improve performance of future classifications without necessitating retraining of the corresponding neural network model. For example, if additional vision sensor data is applied to the same (not further trained) neural network model to generate an additional embedding that is very similar to (or the same as) the generated embedding above, a “hat” classification may be determined (instead of a “bowl” classification) based on the New Classification Embedding for the “hat” classification. Accordingly, classification performance can be quickly improved through adjustment of the classification model, without further computationally intensive and/or time consuming training of the neural network model. Such improved classification performance further leads to improvement in performance of various robotic actions that depend (directly or indirectly) on classifications of objects. 
     In some implementations, in response to determining a human correction, a correction instance can additionally or alternatively be generated and transmitted to one or more remote computing devices. The correction instance can include, for example, the sensor data applied to the neural network model, and correction information that is based on the human correction (e.g., that is indicative of correct feature(s) and/or that indicates determined feature(s) were incorrect). For example, and continuing with the example of the preceding paragraphs, in response to user interface input that indicates the “bowl” classification is incorrect, a correction instance can be generated and transmitted that includes: the vision sensor data applied to the neural network model in generating the incorrect “bowl” classification; and an indication that the “bowl” classification was incorrect. Also, for example and continuing with the example of the preceding paragraphs, in response to user interface input that indicates the alternative “hat” classification is instead the correct classification, a correction instance can be generated and transmitted that includes: the vision sensor data applied to the neural network model in generating the incorrect “bowl” classification; and an indication that “hat” is the correct classification. 
     The correction instance can be transmitted to one or more remote computing devices. The remote computing devices can utilize the correction instance to generate training example(s) for training of one or more neural network model(s), such as neural network model(s) that correspond to those used in determining the incorrect features(s) utilized in performing the action. In various implementations, the training of the neural network model(s) is based on correction instances received from multiple robots. The multiple robots that generate and provide the correction instances can be physically located in disparate geographic locations and/or environments and/or can vary from one another (e.g., vary hardware-wise and/or software-wise). 
     Once a revised version of a neural network model is generated, through training based on correction instances from multiple robots, the revised version of the neural network model can thereafter be utilized by one or more of the multiple robots. For example, one of the multiple robots may utilize the revised version in lieu of an earlier corresponding version that was utilized when one of the correction instances was generated by that robot. For instance, the revised version can be transmitted to that robot and that robot can replace a prior locally stored version with the revised version. In some implementations, a revised version of the features model that corresponds to the revised version of the neural network model can also be generated. For example, the revised version of the features model can include revised feature embeddings for various features (e.g., to correspond to the revised version of the neural network model), can include additional features and corresponding feature embeddings (e.g., where correction instances include “new” features), etc. The revised version of the features model can also thereafter be utilized by one or more of the multiple robots. 
     In some implementations, in response to receiving a “new” version of a neural network model, a robot may adapt a corresponding features model based on past human corrections. As one example, assume that the new version of the neural network model replaces an old version of the neural network model, and a new version of the features model replaces an old version of the features model. Further assume that the old version of the features model was previously updated, in response to a human correction, by adjusting a feature embedding of Feature A to be more similar to an embedding generated over the old version of the neural network model based on Vision Sensor Data X. The new version of the features model may be updated by generating a new embedding over the new version of the neural network model based on applying Vision Sensor Data X to the new version of the neural network model—and adjusting a feature embedding of Feature A (in the new version of the features model) to be more similar to the new embedding. In this manner, prior vision sensor data and prior correction information from past human corrections utilized to update a prior features model of a robot can be utilized by the robot to adjust a new version of the features model via embeddings generated over a new version of the neural network model. 
     In some implementations, determining a human correction of an action of a robot is based on user interface input received in association with performance of the action (i.e., before, during, or after performance of the action). The user interface input can be received via one or more sensors of the robot (e.g., a microphone of the robot, sensor(s) that can be utilized to determine human touch and/or human manipulation of the robot), and/or via a client device that is separate from, but in communication with, the robot (e.g., spoken or typed user interface input received via a tablet or smartphone). 
     Determining that user interface input indicates a correction can be based on analysis of the user interface input itself, and optionally based on feature(s) of the action being performed. For example, it can be based on one or more terms or other cues in the user interface input that indicate a correction (e.g., “no”, “stop”, “not”). Also, for example, it can additionally or alternatively be based on comparison of features(s) determined based on the user interface input to features (s) being utilized in performance of the action. For instance, comparison of “hat” and “bowl” to determine a conflict where the user interface input indicates a classification of an object is a “hat”, whereas the action is being performed based on an incorrectly determined classification of “bowl” for the object. When the user interface input is natural language input (e.g., spoken or free-form typed input), a natural language processing (NLP) system and/or other components may optionally be utilized in determining that such user interface input is a correction. 
     In various implementations, updating a features model in response to human corrections as described herein enables the features model to be adapted in view of so-called “hard negative” instances. That is, the features model is updated as a result of an underlying incorrect determination made based on the pre-updated features model. In other words, the features model is updated based on an action performed by the robot based on what it deemed were correct feature(s), despite the feature(s) being incorrect and/or incomplete in actuality. Such adaptation may be more impactful than updating the features model on so-called “affirmative” instances where the underlying determinations are correct. 
     Further, various implementations that generate and transmit correction instances for utilization in generating training examples for training a neural network model, enable generation of so-called “hard negative” training examples. That is, the training examples are hard negatives since they are generated based on correction instances that are the result of an underlying incorrect determination made based on the neural network model corresponding to the one to be trained. 
     Examples of implementations are provided in the preceding paragraphs for purposes of providing an overview of some of those implementations. However, it is understood that various implementations described herein vary in one or more respects from the provided examples. 
     In some implementations, a method may be provided that includes determining a classification for an object in an environment of a robot. Determining the classification for the object includes: applying, as input to a neural network model locally stored on one or more computer readable media of the robot, sensor data that captures the object and that is generated by at least one sensor of the robot; generating, over the neural network model based on the applying, an embedding of the sensor data; applying the embedding of the sensor data to a classification model locally stored on one or more of the computer readable media of the robot; and determining the classification based on applying the embedding to the classification model. The method further includes receiving user interface input during performance of an action, by the robot, that is directed toward the object. The method further includes determining that the user interface input indicates the classification of the object is incorrect. The method further includes, in response to determining that the user interface input indicates the classification of the object is incorrect: updating the locally stored classification model. Updating the locally stored classification model occurs without retraining of the neural network model. 
     This method and other implementations of technology disclosed herein may each optionally include one or more of the following features. 
     In some implementations, determining that the user interface input indicates the classification of the object is incorrect includes determining that the user interface input indicates an alternative classification, of the object, that conflicts with the determined classification. In some of those implementations, updating the locally stored classification model includes updating the locally stored classification model based on the alternative classification. In some versions of those implementations, updating the locally stored classification model based on the alternative classification includes: identifying a current alternative classification embedding that is mapped to the alternative classification in the locally stored classification model; and determining an adjusted alternative classification embedding based on the alternative classification embedding and based on the embedding of the sensor data. For example, determining the adjusted alternative classification embedding can be based on an average of the alternative classification embedding and the embedding of the sensor data. In some additional or alternative implementations, the method can further include: storing the sensor data and the alternative classification; and subsequent to updating the locally stored classification model: receiving a new version of the neural network model and a new version of the classification model; applying the sensor data as input to the new version of the neural network model; generating, over the new version of the neural network model based on the applying, a new embedding of the sensor data; identifying, in the new version of the classification model, a new alternative classification embedding of the alternative classification; and adjusting, in the new version of the classification model, the new alternative classification embedding of the alternative classification based on the new embedding of the sensor data. In some implementations, updating the locally stored classification model based on the alternative classification includes: determining that the locally stored classification model lacks any alternative classification embedding for the alternative classification; and in response, storing an alternative classification embedding in the locally stored classification model, the alternative classification embedding being based on the embedding of the sensor data. In some implementations, updating the locally stored classification model further includes: applying, as input to the neural network model, additional sensor data that captures the object and that is generated by the sensor of the robot; generating, over the neural network model based on the applying, a further embedding of the additional sensor data; and updating the locally stored classification model based on the further embedding and the alternative classification. 
     In some implementations, determining the classification based on applying the embedding to the classification model includes determining the classification based on determining that the embedding is more similar to a classification embedding mapped to the classification than it is to alternative classification embeddings mapped to alternative classifications. In some of those implementations, updating the locally stored classification model includes: determining an adjusted classification embedding for the classification based on modifying the classification embedding to be less similar to the embedding. 
     In some implementations, a method is provided that includes: applying, as input to a neural network model locally stored on one or more computer readable media of a robot, sensor data that captures an object and that is generated by at least one sensor of the robot; generating, over the neural network model based on the applying, an embedding of the sensor data; and determining a feature of the sensor data based on applying the embedding to a features model locally stored on one or more of the computer readable media of the robot. The features model maps the feature to a feature embedding of the feature and maps additional features to corresponding additional feature embedding. Determining the feature includes determining the feature based on similarity of the embedding of the sensor data to the feature embedding mapped to the feature. The method further includes: performing, based on the determined feature, a robotic action that is directed toward the object; receiving user interface input in response to performance of the robotic action that is directed toward the object; and determining that the user interface input indicates a particular additional feature of the additional features. The method further includes, in response to determining that the user interface input indicates the particular additional feature: adjusting, in the features model, the additional feature embedding of the particular additional feature based on the embedding of the sensor data. 
     This method and other implementations of technology disclosed herein may each optionally include one or more of the following features. 
     In some implementations, the feature is a first classification and the particular additional feature is a second classification. 
     In some implementations, the feature is a first bounding area and the additional feature is a second bounding area. In some of those implementations, the first bounding area is defined by a plurality of first pixels and the second bounding area is defined by a plurality of second pixels. In some of those implementations, the user interface input is generated at a client computing device based on a user indicating the second bounding area via the client computing device. The second bounding can be indicated via the client device during display of an image that is based on the sensor data. In some version of those implementations, the method further includes providing the image to the client device in response to initial user interface input that indicates the robotic action is incorrect. 
     In some implementations, a method is provided and includes: applying, as input to a neural network model locally stored on one or more computer readable media of the robot, sensor data that captures the object and that is generated by at least one sensor of the robot; generating, over the neural network model based on the applying, an embedding of the sensor data; and determining a feature of the sensor data based on applying the embedding to a features model locally stored on one or more of the computer readable media of the robot. The features model maps the feature to a feature embedding of the feature and maps additional features to corresponding additional feature embeddings. Determining the feature includes determining the feature based on the feature embedding mapped to the feature being more similar to the embedding of the sensor data than it is to any of the additional feature embeddings. The method further includes: performing a robotic action based on the determined feature; receiving user interface input in response to performance of the robotic action; and determining that the user interface input indicates the robotic action is incorrect. The method further includes, in response to determining that the user interface input indicates the robotic action is incorrect: updating the locally stored features model based on the embedding of the sensor data; generating a correction instance that includes the sensor data; and transmitting the correction instance to one or more remote computing devices via a network interface. The one or more remote computing devices use the correction instance to generate at least one training example for generating a revised version of the neural network model. 
     This method and other implementations of technology disclosed herein may each optionally include one or more of the following features. 
     In some implementations, the method further includes: receiving, via the network interface, the revised version of the neural network model. The revised version of the neural network model is received after the neural network model is trained based on: the training example, and additional training examples from additional correction instances from additional robots. 
     In some implementations, determining that the user interface input indicates the determined feature is incorrect comprises: determining that the user interface input indicates an additional feature, of the additional features, that conflicts with the determined feature. In some of those implementations, updating the locally stored features model based on the embedding of the sensor data includes: adjusting an additional feature embedding based on the embedding of the sensor data. The additional feature embedding is one of the additional feature embeddings and is mapped to the additional feature in the locally stored features model. In some versions of those implementations, the correction instance includes an indication of the additional feature. In some implementations, updating the locally stored features model based on the embedding of the sensor data includes: determining that the additional feature is not mapped to any of the additional feature embeddings in the locally stored features model; and in response, storing a particular feature embedding for the additional feature in the locally stored features model. The particular feature embedding is based on the embedding of the sensor data. 
     Other implementations may include a non-transitory computer readable storage medium storing instructions executable by a processor to perform a method such as one or more of the methods described above. Yet other implementations may include a system (e.g., a robot and/or one or more other components) including memory and one or more processors operable to execute instructions, stored in the memory, to implement one or more modules or engines that, alone or collectively, perform a method such as one or more of the methods described above. 
     It should be appreciated that all combinations of the foregoing concepts and additional concepts described in greater detail herein are contemplated as being part of the subject matter disclosed herein. For example, all combinations of claimed subject matter appearing at the end of this disclosure are contemplated as being part of the subject matter disclosed herein. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates an example environment in which implementations disclosed herein may be implemented. 
         FIG.  2 A ,  FIG.  2 B ,  FIG.  2 C , and  FIG.  2 D  illustrate examples of providing corrections to robot actions. 
         FIG.  3    is a flowchart illustrating an example method according to implementations disclosed herein. 
         FIG.  4    is a flowchart illustrating another example method according to implementations disclosed herein. 
         FIG.  5    is a flowchart illustrating another example method according to implementations disclosed herein. 
         FIG.  6    is a flowchart illustrating another example method according to implementations disclosed herein. 
         FIG.  7    schematically depicts an example architecture of a robot. 
         FIG.  8    schematically depicts an example architecture of a computer system. 
     
    
    
     DETAILED DESCRIPTION 
       FIG.  1    illustrates an example environment in which implementations disclosed herein may be implemented. The example environment includes a robot  190 . Although a particular robot  190  is illustrated in  FIG.  1   , additional and/or alternative robots may be utilized, including stationary “robot arms”, robots having a humanoid form, robots having an animal form, other robots that move via one or more wheels (e.g., other self-balancing robots, non-self-balancing robots), an unmanned aerial vehicle (“UAV”), and so forth. 
     The robot  190  includes robot arms  194   a  and  194   b  with corresponding grasping end effectors  196   a  and  196   b , that each take the form of a gripper with two opposing actuable members. The robot  190  also includes a base  193  with wheels  197   a  and  197   b  provided on opposed sides thereof for locomotion of the robot  190 . The base  193  may include, for example, one or more motors for driving corresponding wheels  197   a  and  197   b  to achieve a desired direction, velocity, and/or acceleration of movement for the robot  190 . 
     The robot  190  also includes a vision sensor  199 . The vision sensor  199  may be, for example, a stereographic camera, a monographic camera, or a 3D laser scanner. In some implementations, a stereographic camera includes two or more sensors (e.g., charge-coupled devices (CCDs)), each at a different vantage point. Based on sensor data generated by the two sensors at a given instance, vision sensor data that is three-dimensional (“3D”) point cloud data may be generated for the given instance, where each of the 3D points of the 3D point cloud defines a 3D coordinate of a surface of a corresponding object. In some other implementations, a stereographic camera may include only a single sensor and one or more mirrors utilized to effectively capture sensor data from two different vantage points. A monographic camera can include a single sensor and captures two-dimensional (“2D”) vision sensor data. A 3D laser scanner includes one or more lasers that emit light and one or more sensors that generate vision sensor data related to reflections of the emitted light. The generated vision sensor data from a 3D laser scanner may be 3D point cloud data. A 3D laser scanner may be, for example, a time-of-flight 3D laser scanner or a triangulation based 3D laser scanner and may include a position sensitive detector (PSD) or other optical position sensor. 
     As described herein, robot  190  may operate autonomously at least part of the time and control actuators thereof in performance of various actions. For example, in performing various actions, one or more processors of the robot  190  may provide control commands to actuators associated with the wheels  197   a  and/or  197   b , the robot arms  194   a  and/or  194   b , and/or the end effectors  196   a  and/or  196   b.    
     Also illustrated in  FIG.  1    are various components  103  that are utilized in performance of various actions by the robot  190 , in determining human corrections to various actions performed by the robot  190 , in updating features model(s) in response to determining human corrections to those various actions, and/or in generating and transmitting correction instances in response to determining human corrections to those various actions. 
     Although the components  103  are illustrated separate from the robot  190  in  FIG.  1   , connection element  102  indicates that those components  103  can be implemented on robot  190  and/or can be in network communication (e.g., via a local area network and/or a wide area network) with robot  190 . For example, in some implementations, one or more (e.g., all) of the components  103  are implemented by hardware that is local to the robot  190 . For instance, the action system  120 , the local update module  126 , the correction instance engine  130 , and/or the robot data engine  135  may be implemented by one or more processors of the robot  190 . Also, for instance, neural network model  150 A, features model  160 A, neural network model  150 N, features model  160 N, and/or generated robot data database  152  may be stored on one or more hard drives or other computer readable media of the robot  190 . As another example, in some implementations one or more of the components  103  are implemented on one or more computing devices that are remote from the robot  190 . 
     Components  103  include a robot data engine  135  that processes robot sensor data generated by robot sensors of the robot  190  and/or other data generated by components of the robot  190 . For example, the robot data engine  135  may receive robot sensor data from various sensors of the robot, timestamp the robot sensor data, and provide the timestamped robot sensor data to the action system  120 . The robot sensor data may include, for example, vision sensor data from vision sensor  199 , position sensor data from position sensors of actuators of the robot, accelerometer data from accelerometer(s) of the robot, etc. The robot data engine  135  may further store, in generated robot data database  152 , a log of at least some of the robot sensor data. For example, the robot data engine  135  may store a buffer of “recent” sensor data, clearing data from the buffer based on space, temporal, and/or other factors. The robot data engine  135  may further store, in generated robot data database  152 , various other data generated by the robot  190  such as features determined by action system  120 , control commands provided to actuators of the robot  190 , etc. 
     The action system  120  uses robot sensor data provided by the robot data engine  135  in performing various robotic actions. The actions may include actions such as picking up an object, “picking and placing” an object, or navigating to a location—and/or relatively more complex collections of actions such as unloading a dishwasher, picking up all “toys” from a defined area, clearing a table of all objects, clearing a table of only certain type(s) of objects, retrieving certain types of objects from multiple rooms of a building, etc. The action system  120  may perform robotic actions in response to various cues. For example, the action system  120  can perform some robotic actions in response to user interface input provided by a human (e.g., perform a task “on demand” in response to spoken input). As other examples, the action system  120  may additionally or alternatively provide some actions based on a schedule (e.g., every weekday morning) and/or based on environmental conditions (e.g., remove items from an area when “messy”). 
     The action system  120  can utilize various modules in performance of actions, examples of which are illustrated in  FIG.  1   . Features modules  121 A-N of action system  120  determine various features for use by planning module  122  in determining how to execute an action. For example, features modules  121 A-N can determine features that indicate poses (positions and/or orientations) of objects in the environment, features that indicate classes of objects in the environment of the robot  190 , features that indicate how to grasp objects in the environment of the robot  190  (e.g., a bounding box that indicates a grasp pose for grasping end effector  196   a  and/or  196   b ), features that indicate weights, materials, and/or other physical characteristics of objects in the environment of the robot  190 , etc. The planning module  122  can utilize such features in determining how to execute an action. For example, in clearing “dishes” from a table, the planning module  122  can use determined object poses and classifications to determine objects that are “dishes” and that are “on the table”, and can utilize grasping features to determine how to grasp those objects in removing them from the table. 
     The commands module  123  generates control commands to provide to actuators of the robot  190  to effectuate actions determined by planning module  122 . For example, the planning module  122  can be a higher level planner that generates paths and other higher level movements for performing an action, and the commands module  123  can be a real-time module that generates real-time control commands to provide to actuators of the robot  190  to effectuate those higher level movements. 
     Two features modules  121 A and  121 N are illustrated in  FIG.  1   . Features module  121 A utilizes a corresponding neural network model  150 A and features model  160 A in generating corresponding features. Feature module  121 N utilizes a corresponding neural network model  150 N and features model  160 N in generating corresponding features. It is understood that additional features modules may be provided, as indicated by the ellipsis in between features modules  121 A and  121 N. Further, one or more of those additional features modules may optionally utilize a corresponding neural network model and optional features model, as indicated by the additional ellipsis. 
     As one example, features module  121 A can utilize neural network model  150 A and features model  160 A to effectively perform object recognition. For instance, the features module  121 A can apply an instance of vision sensor data (provided by robot data engine  135 ) as input to neural network model  150 A and generate an embedding over the model  150 A based on the input. The features module  121 A can then apply the generated embedding to the features model  160 A to determine one or more poses that are most similar to the generated embedding. For example, the features model  160 A may define bounding areas (e.g., a bounding box) that are each mapped to a corresponding feature embedding of the features model  160 A, and the features module  121 A can determine the feature embedding(s) with the greatest similarity (e.g., shortest distance to) the embedding generated over the neural network model. The features module  121 A can use the bounding area(s) mapped to those feature embedding(s) to determine poses(s) of object(s) captured by the vision sensor data. For example, a bounding area may define a bounding box of pixels of the vision sensor data, and those pixels utilized to determine the pose of the object (e.g., the pose can correspond to the effective position of those pixels in the environment). This may be performed iteratively for each of multiple instances of vision sensor data to enable the features module  121 A to determine poses for each of a plurality of objects in the robots environment—effectively enabling the features module  121 A to maintain an up to date spatio-temporal “inventory” of objects in the robot&#39;s environment. 
     As another example, features module  121 N can utilize neural network model  150 N and features model  160 N to effectively perform object classification of objects captured by vision sensor data. For example, to determine the classification of an object, the features module  121 N can apply vision sensor data as input to neural network model  150 N and generate, over the model  150 N based on the input, an embedding of the vision sensor data. The features module  121 N can then apply the generated embedding to the features model  160 N to determine one or more classifications based on the generated embedding. For example, the features model  160 N may define classifications that are each mapped to a corresponding classification embedding of the features model  160 N, and the features module  121 N can determine the classification embedding(s) with the greatest similarity (e.g., shortest distance to) the embedding generated over the neural network model. The features module  121 N can use the classification(s) mapped to those classification embedding(s) to determine classification(s) of object(s) captured by the vision sensor data. In some implementations, the features module  121 N can optionally also provide, as input to the neural network model  150 A along with the vision sensor data, a pose of a particular object whose classification is to be determined (e.g., a pose determined by features module  121 A). This can be used to generate an embedding that is based on the vision sensor data, but tailored particularly to the particular object (e.g., based on its position in the vision sensor data, as indicated by the pose). 
     Although particular examples of neural network models, features models, and features modules are provided in the preceding paragraphs, it is understood that additional and/or alternative neural network models, features models, and/or features modules may be provided. For example, an additional features module may apply vision sensor data and/or other data (e.g., feature(s) of other features module(s)) as input to a corresponding trained neural network model and generate, over the model based on the input, an embedding that can then be applied to a corresponding features model to determine grasping features that indicate where to grasp an object captured by the vision sensor data. For instance, the grasping features may be two grasping points indicated by two corners of a bounding box mapped to a feature embedding in the features model. Also, for example, one or more features modules may apply non-vision sensor data as input to a corresponding trained neural network model and generate, over the model based on the input, an embedding that can be applied to a corresponding features model to determine one or more features related to an environment of a robot. The non-vision sensor data can be from one or more of various sensors such as position sensor(s), tactile sensors, audio sensors (e.g., microphones), etc. The non-vision sensor data can optionally be applied as input along with vision sensor data that is also applied as input. For instance, the trained neural network model may be trained to accept, as input, both vision sensor data and non-vision sensor data. 
     The action system  120  also includes a UI output module  125 . The UI output module  125  can generate user interface output to provide during performance of an action by the robot  190 . The provided user interface output is in addition to the movements of the robot  190  in furtherance of performing the action, and provides a perceptible indication of one or more features determined by the features modules  121 A-N. For example, the user interface output can be audible output provided via a speaker of the robot  190 , where the audible output “speaks” a determined feature of an object being acted upon (or to be acted upon) by the robot  190 . For instance, when the robot  190  is picking up an object classified by one of the features modules  121 A-N as a “plate”, the robot  190  may generate audible output of “picking up the plate.” In other implementation, the UI output module  125  may additionally or alternatively provide visual and/or other non-audible user interface output. Providing user interface output that indicates a determined feature of an object enables its perception by a human in the environment of the robot  190 . Perception of the user interface output enables the human to recognize if it is incorrect and, if so, provide a correction as described herein. 
     The action system  120  also includes a UI input module  124  that receives user interface input. For example, the UI input module  124  can receive spoken user interface input provided via a microphone of the robot  190 , or user interface input provided by a client device  106  that is in communication with the UI input module  124 . In some implementations, hardware associated with any microphone of the robot  190  may only be “actively listening” for a human correction in response to certain preceding input, such as a spoken “hot word” (e.g., “hey robot”), a selection of a hardware button (e.g., on the robot  190 ), a selection of a virtual button (e.g., rendered on the client device  106 ), etc. 
     User interface input provided by the client device  106  is provided in response to user interaction with input device(s) of the client device  106  (e.g., a microphone of the client device  106  and/or a virtual or hardware keyboard), and may optionally be pre-processed by the client device  106  and/or other component (e.g., spoken input pre-processed by converting it to textual input). In some implementations, the UI input module  124  can generate one or more features for use by the action system  120  based on the received user interface input. For example, for received user interface input of “clear the table”, the UI input module  124  can process the input to determine features that indicate actions should be performed, by the robot  190 , to remove all objects that are on a “table” object in the environment of the robot  190 . Any features determined by the UI input module  124  can be provided to the planning module  122 . The UI input module  124  can interact with NLP system  133  and/or one or more other components in determining features from received user interface input. 
     The UI input module  124  can additionally or alternatively work in conjunction with the correction instance engine  130 . The correction instance engine  130  determines whether received user interface input indicates a correction to a robot action. If so, the correction instance engine  130  can cause the local update module  126  (described in more detail below) to perform a corresponding local update to one of the features models  160 A-N and/or can itself generate and transmit a corresponding correction instance to collection engine  140  via network  101 . In this manner, in various implementations the correction instance engine  130  may cause local update module  126  to perform a corresponding local update to one of the features models  160 A-N, and/or may transmit correction instances, only when received user interface input indicates a correction to the robot action. 
     The correction instance engine  130  can utilize various techniques in determining that received user interface input indicates a correction to a robot action. In some implementations, where the received user interface input is natural language input (e.g., received via microphone of the robot  190  or received via the client device  106 ), the correction instance engine  130  can determine it indicates a correction based on semantic and/or other natural language processing of the natural language input. The correction instance engine  130  may optionally interact (e.g., via the Internet or other network(s)) with NLP system  133  or other external resource(s) in processing of natural language input. The NLP system  133  can process natural language input and provide various annotations of the natural language input. For example, the NLP system  133  can provide sentiment annotations (e.g., using a sentiment classifier), entity annotations (that annotate included entities generally and/or specifically), annotations that provide a full semantic interpretation of natural language input, etc. 
     In some implementations, correction instance engine  130  determines that user interface input indicates a correction to an action based on comparison of the input to a feature used by the action system  120  in performance of the action. For example, assume that natural language input of “that&#39;s a bowl” is received while the robot  190  is interacting with an environmental object that the features module  121 N has determined is a “cup”. The correction instance engine  130  may determine that the user interface input indicates a correction to the action based on the conflict between the “bowl” classification indicated by the natural language input and the “cup” classification determined by the features module  121 N. As described herein, in some implementations the UI output module  125  may provide audible or other user interface output during interaction with the environmental object, which may prompt a human to provide the user interface input of “that&#39;s a bowl”. 
     In some implementations, correction instance engine  130  determines a correction to an action of the robot based on use interface input that is not natural language user interface input. For example, the user interface input can be based on the user actuating an “error” button on the robot  190 , the user “grabbing” the robot  190  (e.g., as sensed based on “exterior” touch-sensitive sensors and/or force-torque or other “internal” sensor(s)), the user interacting with a graphical user interface via the client device  106  (e.g., an interface that enables the user to monitor the status of the robot  190  and report errors generally and/or specifically). 
     The correction instance engine  130  interacts with the local update module  126  of action system  120  to cause the local update module  126  to update one or more of the features models  160 A in response to determining a human correction of an action performed by the robot  190 . For example, the correction instance engine  130  can provide, to the local update module  126 , and indication that a feature is incorrect and/or that an additional feature is instead correct (or is also correct). In response, the local update module  126  can update a corresponding features model based on the embedding (generated over a corresponding neural network model) that was utilized to determine the incorrect feature. For instance, assume features module  121 N applied vision sensor data as input to neural network model  150 N, generated Embedding A based on the input, and determined Feature A based on applying Embedding A to the features model  160 N. Further assume the correction instance engine  130  indicates that Feature A is incorrect and that Feature B is instead correct. The local update module  126  may update the features model  160 A to cause Feature B Embedding for Feature B to be more similar to Embedding A and/or to cause Feature A Embedding for Feature A to be less similar to Embedding A. 
     In some implementations, the correction instance engine  130  additionally and/or alternatively generates a correction instance in response to determining a human correction of an action performed by the robot  190 . The correction instance generated by correction instance engine  130  in response to determining a correction can include, for example, the sensor data applied to the neural network model associated with the human correction (i.e., that utilized to generate the incorrect or incomplete feature(s)), and correction information that is based on the human correction (e.g., that is indicative of correct feature(s) and/or that indicates determined feature(s) were incorrect). 
     The collection engine  140  receives correction instances from correction instance engine  130  via network  101  (e.g., the Internet). The collection engine  140  may also receive correction instances from additional robots  192  via the network  101 . The additional robots  192  can be in various geographic locations and the additional robots  192  may optionally vary from the robot  190  (e.g., they may be different “types”, such as a type with four wheels). The correction instances provided by the additional robots  192  can include those that are also generated in response to a human correction of a robot action. The collection engine  140  can be implemented by, for example, a cluster of computing devices. 
     The collection engine  140  utilizes the correction instances that correspond to a given neural network model and features model to generate training examples for training of the neural network model and the features model. As one example, assume a correction instance is received that includes an instance of vision sensor data that capture a particular object, an indication that the particular object was incorrectly classified as a “bowl”, and an indication that the classification was corrected to “hat” (via a human correction). In such an example, the collection engine  140  can generate a training example that includes: training example input that is based on a corresponding instance of the vision sensor data; and training example output that indicates “bowl” is not a correct classification and/or that “hat” is a correct classification. 
     The collection engine  140  provides generated training examples to training engine  145 . The training engine  145  utilizes the training examples to train one or more corresponding neural network models and features models to generate corresponding revised neural network models  151 A-N and revised features models  161 A-N. As one example, assume the collection engine  140  provides a large number of training examples generated based on human corrections to incorrect classifications of objects by one or more instances of neural network model  150 N and features model  160 N. In such an example, the training engine  145  may further train the neural network model  150 N (or another model similar to neural network model  150 N) and features model  160 N to generate revised neural network model  151 N and revised features model  161 N. For example, the training engine  145  can apply training example input of a training example to the neural network model  150 N, generate an embedding based on the training example, apply the embedding to the features model  160 N, determine an error based on the feature indicated by the training example output and the actual feature indicated based on the application of the embedding to the features model  160 N, and backpropagate the error through the features model  160 N and/or the neural network model  150 N to update the features model  160 N and/or the neural network model  150 N. Batch training may optionally be utilized. 
     Through application of a large quantity of training examples, the training engine  145  generates a revised neural network model  151 N and/or a revised features model  161 N. The revised neural network model  151 N and/or revised features model  161 N can then be provided for utilization in lieu of the neural network model  150 N and/or the revised features model  161 N. Additionally, such revised neural network model  151 N and/or revised features model  161 N can further be revised by the training engine  145  in the future, based on subsequently generated training examples, to generate a further revised version of the revised neural network model  151 N and/or revised features model  161 N that can then be provided for utilization. In this manner, improved neural network models and features models may be iteratively trained and provided, using training examples that are based on correction instances from robots  190  and  192  that are in diverse environments. The training engine  145  can be implemented by, for example, a cluster of computing devices. 
     In some implementations, in response to receiving a “new” version of a neural network model, the local update module  126  of the robot  190  may adapt a corresponding features model based on past human corrections. As one example, assume that a revised neural network model  151 N replaces neural network model  150 N, and a revised features model  161 N replaces features model  160 N. Further assume that local update module  126  previously updated features model  160 N, in response to a human correction, by adjusting a feature embedding of Feature A (of features model  160 N) to be more similar to an embedding generated over neural network model  150 N based on Vision Sensor Data X. The local update module  126  may locally store features of the previous update, such as Vision Sensor Data X and an indication of “Feature A”, and utilize the stored previous update to update the revised features model  161 N. For example, the local update module  126  can update the revised features model  161 N by generating a new embedding over the revised neural network model  151 N based on applying Vision Sensor Data X to the revised neural network model  151 N—and adjusting, in the revised features model  161 N, a feature embedding of Feature A (of revised features model  161 N) to be more similar to the new embedding. 
     With reference now to  FIGS.  2 A- 8   , additional description of various components of the example environment of  FIG.  1    are provided. 
     Turning initially to  FIGS.  2 A- 2 D , some examples of providing corrections to robot actions are provided.  FIGS.  2 A- 2 D  depict various states of an example environment in which the robot  190  and/or other robot(s) may operate.  FIG.  2 A  illustrates a table  250  at a first time. At the first time, four objects  252   A-D  are placed on top of the table  250 . 
       FIG.  2 B  illustrates an example where, at the first time of  FIG.  2 A , a user has provided user interface input  103 B 1  of “clear the bowls from the table”. In response, the robot  190  (not depicted in  FIG.  2 B ) may begin removing, from the table  250 , objects that it has classified as “bowls”. For example, it can begin removing any object classified as a “bowl” by the features module  121 N (based on application of an embedding, generated over neural network model  150 N, to features model  160 N). For instance,  FIG.  2 B  illustrates the environment when the robot  190  is grasping and removing object  252   D  from the table  250 , which it has incorrectly classified as a “bowl”. While the robot  190  is interacting with the object  252   D , the user provides further user interface input  103 B 2  of “not the hat”. 
     The correction instance engine  130  can determine the further user interface input  103 B 2  indicates a correction to the action being performed on the object  252   D . For example, the correction instance engine  130  can determine that the “hat” classification, indicated by the user interface input  103 B 2 , conflicts with the incorrectly determined “dish” classification. In response, the correction instance engine  130  can interact with the local update module  126  to cause the local update module  126  to update the features model  160 N. For example, the correction instance engine  130  can provide, to the local update module  126 , an indication that the “bowl” classification was incorrect and that “hat” is instead the correct classification. In response, the local update module  126  can update the features model  160 N based on the embedding, generated over neural network model  150 N, that was utilized to determine the incorrect feature. For instance, if a feature embedding for a “hat” classification is already present in the features model  160 N, the local update module  126  can adjust that feature embedding to make it more similar to the embedding generated over the neural network model  150 N that was utilized to determine the incorrect “bowl” classification. Also, for instance, if a feature embedding for a “hat” classification is not present in the features model  160 N, the local update module  126  can generate a feature embedding for “hat” in the features model  160 N. For instance, the feature embedding for “hat” can be based on the embedding generated over the neural network model  150 N that was utilized to determine the incorrect “bowl” classification. As described herein, in some implementations the correction instance engine  130  may additionally and/or alternatively generate and transmit a correction instance based on determining that the further user interface input  103 B 2  indicates a correction to the action being performed on the object  252   D . 
       FIG.  2 C  illustrates an example of interactions between components of  FIG.  1    that may occur during the example of  FIG.  2 B . In  FIG.  2 C , the features module  121 A applies vision sensor data as input to the neural network model  150 A, and generates an embedding based on that application. The features module  121 A further applies the embedding to the features model  160 A and determines a classification of “bowl” based on that application. For example, the features module  121 A can determine the “bowl” classification based on the embedding being more similar to Feature embedding B of the features model  160 A (illustrated in  FIG.  2 C  as mapped to “bowl”) than it is to any other of the Feature Embeddings A and C-N. 
     The features module  121 A provides, to planning module  122 , an indication that “object D” (corresponding to the hat of  FIGS.  2 A and  2 B ) has a “bowl” classification. For example, the features module  121 A can provide, to the planning module  122 , a pose and/or other unique identifier of “object D”, along with an indication of its classification. 
     The planning module  122  utilizes the classification for “object D” to determine one or more actions to be performed. In determining the action(s), the planning module  122  may also rely on other classifications determined by features module  121 A and/or other features from other features module(s). The planning module  122  provides the actions to commands module  123 , which generates and provides control commands to one or more actuators (not illustrated in  FIG.  2 C ) to effectuate performance of the action. 
     During or after performance of the action, UI input module  124  receives UI input and provides the UI input (and optionally annotations of the UI input) to correction instance engine  130 . Correction instance engine  130  utilizes the UI input to determine that the classification for “object D” is incorrect, and should instead be a “hat” classification. 
     The correction instance engine  130  provides an indication of the correct “hat” classification to local update module  126 . In response, the local update module  126  updates Feature Embedding C (illustrated in  FIG.  2 C  as mapped to “hat”) to be more similar to the embedding generated by the features module  121 A in incorrectly determining the “bowl” classification. 
       FIG.  2 D  illustrates an example where, at the first time of  FIG.  2 A , a user has provided user interface input  103 D 1  of “clear the table” (instead of providing the user interface input  103 B 1  of  FIG.  2 B ). In response, the robot  190  (not depicted in  FIG.  2 D ) can remove, from the table  250 , all objects that is has identified. For example, it can remove any object detected as an object by the features module  121 A. For instance, the features module  121 A can apply one or more instances of vision sensor data to the neural network model  150 A and generate a corresponding embedding based on each application. The features module  121 A can further apply each embedding to the features model  160 A to determine one or more bounding boxes that each indicate a pose of a corresponding object on the table  250 . For example, features model  160 A may map Feature Embedding A to a combination of Bounding Box A and Bounding Box B. Features module  121 A can determine that a given embedding (generated over the neural network model  150 A) is most similar to Feature Embedding A and, as a result, select Bounding Box A and Bounding Box B (based on those bounding boxes being mapped to Feature Embedding A). The features module  121 A can detect an object (e.g.,  252   A ) based on Bounding Box A and determine it has a pose corresponding to Bounding Box A. The features module  121 A can also detect an object (e.g.,  252   C ) based on Bounding Box B and determine it has a pose corresponding to Bounding Box B. Object  252   D  may also be detected based on an additional embedding that is based on additional vision sensor data. 
       FIG.  2 D  illustrates the environment when the robot  190  has completed removing all objects that it has identified. However, the object  252   B  remains on the table  250  due to it not being recognized, by the robot  190 , as an object that is separate from the table  250 . For example, the features module  121 A may have failed to detect the object  252   B . For instance, a bounding box that corresponds to the pose of the object  252   B  may be included in the features model  160 A and mapped to Feature embedding X. However, the features module  121 A may not have selected that bounding box based on embeddings (generated over neural network model  150 A) not having at least a threshold degree of similarity to Feature embedding X. 
     In  FIG.  2 D , the user provides further user interface input  103 D 2  of “you forgot the fork”. In some situations, the user may provide the further user interface input  103 D 2  in response to audible user interface output (e.g., “I&#39;m done”, a “chime”) provided by the robot  190  to indicate it has completed removing all objects that it has identified. 
     The correction instance engine  130  can determine the further user interface input  103 D 2  indicates a correction to the action being performed. For example, the correction instance engine  130  can determine that the user interface input  103 D 2  indicates that an object present on the table  250  was not recognized. 
     In response, the correction instance engine  130  can interact with the local update module  126  to cause the local update module  126  to update the features model  160 N. In response, the local update module  126  requests further correction details via the client device  106 . As illustrated in  FIG.  2 D , the local update module  126  provides (via network) an image of the table  250  to the client device  106  and requests that the user draw a box around the “fork”. The image of the table  250  can be based on, for example, an instance of vision sensor data on which the incomplete objects were detected. The user provides user interface input to draw a box  107  around the fork (e.g., using a touch-screen of the client device  106 ). The local update module  126  can identify, in local model  160 A, a corresponding bounding box that corresponds to box  107 . Further, the local update module  126  can update a feature embedding, that is mapped to the corresponding bounding box, to cause that feature embedding to be more similar to an embedding generated over the neural network model  150 A (e.g., an embedding generated based on the vision sensor data utilized to generate the image of the table  250  on the client device  106 ). In this manner, the local update module  126  solicits input from the user for the correct feature (the bounding box), and updates a feature embedding for that feature in the features model  160 A based on an embedding generated over the neural network model  150 . 
     As described herein, in some implementations the correction instance engine  130  may additionally and/or alternatively generate and transmit a correction instance based on determining that the further user interface input  103 D 2  indicates a correction to the action. 
     Referring now to  FIG.  3   , an example method  300  according to various implementations is described. For convenience, the operations of the flowchart are described with reference to a system that performs the operations. This system may include various components of a robot and/or of one or more computing devices in communication with the robot. Moreover, while operations of method  300  are shown in a particular order, this is not meant to be limiting. One or more operations may be reordered, omitted or added. 
     At block  352 , the system generates an embedding over a neural network model based on application of robot sensor data to the neural network model. As a working example, the system can apply, as input to the neural network model, vision sensor data that captures an object. 
     At block  354 , the system determines at least one feature based on a comparison of the embedding to a feature embedding mapped to the feature in a features model. Continuing with the working example, the features model can map grasping parameters to corresponding feature embeddings. For example, Grasping Parameters A can be mapped to Feature embedding A, Grasping Parameters B can be mapped to Feature embedding B, etc. The system may select Feature embedding A based on similarity of that feature embedding to the embedding generated at block  352 . 
     It is noted that in various implementations the system can determine feature(s) at block  354  based on multiple instances of output generated based on multiple instance of block  352 , with each instance of block  352  applying a different instance of sensor data. Also, it is noted that in various implementations the system can determine feature(s) at block  354  based on feature(s) determined based on additional output generated over other neural network model(s) that are in addition to that of block  352 . 
     At block  356 , the system performs a robotic action based on the at least one feature generated at block  354 . Continuing with the working example, the system can perform all or part of grasping of the object, with the determined grasping parameters. 
     Block  356  includes sub-blocks  356 A and  356 B. At block  356 A the system monitors for completion of the action. If the system determines the action is completed, the system proceeds to block  358  and begins performance of a next action. 
     At block  356 B, the system monitors for receipt of correction user interface input. The system can monitor for the receipt of correction user interface input before, during, or after (e.g., for at least a threshold duration after) the performance of the action. If correction user interface input is received, the system proceeds to block  360 . 
     Continuing with the working example, assume the system has grasped the object and, while still grasping the object, user interface input of “not like that” is received. In such an example, the system can determine “not like that” is correction user interface input, and proceed to block  360 . It is noted that in many scenarios, the system receives correction UI input at block  356 B even though the performance of the action, from the system&#39;s perspective, is correct. In other words, absent the correction user interface input, the system would not self-recognize the incorrect nature of the performance of the action. 
     At block  360 , the system adjusts the feature embedding of the features model based on the correction user interface input. Continuing with the working example, in response to the correction instance indicating the grasp, based on the grasping parameters mapped to Feature Embedding A, is incorrect—the system can adjust Feature Embedding A to be less similar to the embedding generated at block  352 . 
     In some implementations, block  360  includes sub-block  360 A, in which the system requests and receives further correction details. Continuing with the working example, the system may provide user interface output of “can you show me the right way to pick it up?”. In response, the user can kinesthetically teach the correct grasp by physically manipulating the robot. For instance, the system may cause the robot to be in a low mechanical impedance, “gravity-compensated” mode, during which the user can physically manipulate the robot to demonstrate the correct grasp. In such an instance, the system can record sensor data to determine the correct grasping parameters for the object. The system can then adjust a feature embedding of the features model that corresponds to the correct grasping parameters. For example, the system can adjust that feature embedding to be more similar to the embedding generated at block  352 . 
     At optional block  362 , the system generates a correction instance. The correction instance can include vision sensor data and/or other robot sensor data that is relevant to the correction. For example, the system can include certain robot sensor data based on it being applied at block  352  to generate the embedding. In some implementations, the system also includes in the correction instance: the feature(s) determined at block  354 , correction information that is based on the correction user interface input received at block  356 B and/or the further correction details received at block  360 A, and/or other data. 
     Continuing with the working example, the system can provide a correction instance that includes the vision sensor data applied at block  352 , and that includes the correct human demonstrated grasping features determined at block  360 A. 
     At optional block  364 , the system transmits the correction instance generated at block  362 . For example, the system can transmit the correction instance to a remote computing device via one or more networks. 
     At block  366 , the system receives a revised neural network model that is trained based on the correction instance, and receives a revised features model for the revised neural network model. In some implementations, the revised neural network model and revised features model are trained based on training example(s) generated based on the correction instance and based on other training examples generated based on other correction instances from a plurality of additional robots. It is understood that in many implementations there will be a time delay (e.g., hours, days, weeks) between block  364  and block  366 . In the interim, the system may continue to utilize the “prior” neural network model and its locally updated features model in performing other actions. 
     At optional block  368 , the system adjusts the revised features model based on past corrections, such as the correction determined at block  360 . For example, the system may have stored the robot sensor data applied at block  352 , as well as the correction information determined at block  360 . The system may apply the stored robot sensor data as input to the revised neural network model, generate an embedding over the revised neural network model based on the input, and adjust feature embedding(s) of the revised features model based on the correction details and the embedding. Continuing with the working example, an embedding can be generated over the revised neural network model based on applying the vision sensor data, and the feature embedding mapped (in the revised features model) to the “correct grasping parameters” updated to be more similar to the generated embedding. 
     The system can utilize the revised neural network model and the (optionally revised at block  366 ) features model in lieu of prior versions of those models. 
     Referring now to  FIG.  4   , another example method  400  according to various implementations is described. It is noted that method  400  illustrates a particular implementation of the method  300  of  FIG.  3   . 
     For convenience, the operations of the flowchart of  FIG.  4    are described with reference to a system that performs the operations. This system may include various components of a robot and/or of one or more computing devices in communication with the robot. Moreover, while operations of method  400  are shown in a particular order, this is not meant to be limiting. One or more operations may be reordered, omitted or added. 
     At block  452 , the system generates an embedding over a neural network model based on application of vision sensor data to the neural network model. 
     At block  454 , the system determines at least one classification of an object captured by the vision sensor data. The system determines the classification based on comparison of the embedding generated at block  452  to a classification embedding mapped to the classification in a classification model. A classification model is one particular type of features models that maps classification embeddings (a particular type of feature embeddings) to corresponding classifications. 
     At block  456 , the system performs a robotic action directed toward the object. For example, the system can move one or more of components of a robot closer toward and/or in contact with the object in grasping or otherwise contacting the object, in getting a better view of the object, etc. 
     Block  456  optionally includes blocks  456 A and/or  456 B. 
     At block  456 A, the system performs the robotic action in response to user interface input indicating a classification of the object. For example, the system can perform the action directed toward the object based on received user interface indicating the classification for the object determined at block  452 . As a working example, in response to user interface input of “find the remote control”, the system can perform an action directed toward an object it has determined has a “remote control” classification. 
     At block  456 B, the system provides user interface output indicating the classification for the object determined at block  456 B. For example, before and/or during performance of the robotic action, the system can provide audio output of “picking up the remote control”. 
     At block  458 , the system determines that received user interface input indicates correction of the robotic action directed towards the object. Continuing with the working example, in performing a robotic action of picking up what the system has deemed a “remote control”, the system can receive user interface input of “not the phone”. The system can determine the received user interface input indicates a correction based on the presence of “not” and/or based on determining a conflict between “phone” and “remote control”. 
     At block  460 , the system adjusts the classification embedding of the classification model based on the correction UI input. Continuing with the working example, in response to the correction instance the system can adjust the classification embedding mapped to the “remote control” classification to be less similar to the embedding of block  452 . Additionally or alternatively, the system can adjust the classification embedding mapped to the “phone” classification to be more similar to the embedding of block  452 . 
     In some implementations, block  460  includes sub-block  460 A, in which the system requests and receives further correction details. For example, if the received user interface input of block  458  was “no, not that” (and didn&#39;t identify “the phone”), the system may provide user interface output of “can you tell me what I incorrectly picked up?”. In response, the user can provide further spoken input of “the phone”. Based on such further spoken input, the system can adjust the classification embedding mapped to the “phone” classification to be more similar to the embedding of block  452 . 
     At optional block  462 , the system generates a correction instance that includes the vision sensor data applied at block  452 . In some implementations, block  462  includes sub-blocks  462 A and/or  462 B. At block  462 A, the system includes the determined classification of the object in the correction instance. That is, the determined classification that was incorrectly determined at block  454 . At block  462 B, the system includes correction information in the correction instance. The correction information can include, for example, an indication of a human provided classification provided at block  458  or block  460 A. Additional and/or alternative data may optionally be included by the system in the correction instance. 
     At block  464 , the system transmits the correction instance generated at block  460 . For example, the system can transmit the correction instance to a remote computing device via one or more networks. 
     At block  466 , the system receives a revised neural network model that is trained based on the correction instance, and a revised classification model that is trained based on the correction instance (and/or trained in view of the revised neural network model). In some implementations, the revised neural network model and/or the revised classification model are trained based on training example(s) generated based on the correction instance and based on other training examples generated based on other correction instances from a plurality of additional robots. In some implementations, the system may adjust the received revised classification model based on past corrections, such as the correction determined at block  458 . 
     Referring now to  FIG.  5   , yet another example method  500  according to various implementations is described. For convenience, the operations of the flowchart are described with reference to a system that performs the operations. This system may include various components of a robot and/or of one or more computing devices in communication with the robot. Moreover, while operations of method  500  are shown in a particular order, this is not meant to be limiting. One or more operations may be reordered, omitted or added. 
     Method  500  of  FIG.  5    illustrates an example of generating a revised version of a neural network model and a revised version of a features model based on correction instances, such as correction instances provided at block  364  ( FIG.  3   ) and/or block  464  ( FIG.  4   ). 
     At block  552 , the system receives correction instances from multiple robots. In some implementations, the received correction instances are all applicable to the same given neural network model. For example, the received correction instances may all be applicable to a neural network model utilized in classification of objects. 
     In some other implementations, the correction instances received at block  552  may collectively be applicable to various neural network models. For example, some correction instances may be applicable to a “classification” neural network model, other correction instances may be applicable to a “grasping” neural network model, etc. In some of those implementations, block  552  includes sub-block  552 A, in which the system determines correction instances that are applicable to a given neural network model. In other words, at block  552 A, the system may determine, from a group of correction instances applicable to various neural network models, a sub-group that is applicable to the given neural network model. At block  552 A, the system can determine the correction instances based on analysis of content of the correction instances. For example, the system can determine such correction instances based on the correction instances explicitly indicating the given neural network model (or a corresponding version). Also, for example, the system can determine such correction instances additionally or alternatively based on correction information included in such correction instances indicating the given model. 
     At block  554 , the system selects a correction instance for the given neural network model. 
     At block  556 , the system generates and stores one or more training examples based on the selected correction instance. 
     At block  558 , the system determines if an additional correction instance is available for the given neural network model. If so, the system proceeds to block  554  and selects an additional correction instance, then proceeds again to block  556  and generates and stores additional training example(s) based on the selected additional correction instance. This may be iteratively performed, and may optionally be performed in parallel with block  560  (described below). 
     At block  560 , the system generates a revised version of the neural network model and a revised version of a corresponding features model by training based on the training examples. For example, the system can start with the same versions of the neural network model and the features model that was utilized by the robots that provided the correction instances of block  552 , different versions of the same models, or different (but functionally similar) models. In some implementations, the system generates the revised versions by training until one or more criteria are satisfied. The criteria can include, for example, use of all “current” training examples, at least a threshold quantity of training examples being utilized, at least a threshold duration of training being achieved, etc. 
     At block  562 , the system provides, to one or more robots, the revised version of the given neural network model and the revised version of the features model for use in lieu of the prior versions. 
     Referring now to  FIG.  6   , yet another example method  600  according to various implementations is described. For convenience, the operations of the flowchart are described with reference to a system that performs the operations. This system may include various components of a robot and/or of one or more computing devices in communication with the robot. Moreover, while operations of method  600  are shown in a particular order, this is not meant to be limiting. One or more operations may be reordered, omitted or added. 
     Method  600  of  FIG.  6    illustrates an example of adjusting a features model for a received revised version of a neural network model, where the features model is adjusted based on past corrections associated with past version(s) of the neural network model. For example, method  600  illustrates an example of implementations of block  368  of  FIG.  3   . 
     At block  652 , the system receives a revised version of a neural network model and a features model that corresponds to the revised version of the neural network model. 
     At block  654 , the system selects past vision sensor data and corresponding correction detail(s) for the past vision sensor data. The past vision sensor data and correction detail(s) are previously stored by the system in response to a prior human correction determined based on a prior version of the neural network model. For example, the correction detail(s) may include a correct classification for an object captured by the vision sensor data and may be based on the prior human correction. The prior human correction may have been utilized to update a prior version of the features model. For example, the system may have previously adjusted a feature embedding, of the prior version of the features model, to make it more similar to an embedding generated over the prior version of the neural network model based on the vision sensor data. 
     At block  656 , the system generates an embedding based on applying the vision sensor data to the revised version of the neural network model. It is understood that the embedding generated at block  656  will likely differ from the embedding previously generated over the prior version of the neural network model, due to the revised version of the neural network model being further trained. 
     At block  658 , the system updates, based on the embedding of block  656 , the feature embedding(s) mapped to the correction detail(s) in the features model. For example, where the correction detail(s) of block  654  include a correct classification, the classification embedding mapped to that correct classification may be adjusted to be more similar to the embedding of block  656 . 
     At block  660 , the system determines whether an additional past vision senor data and corresponding correction detail(s) instance remains. If so, the system proceeds back to blocks  654 ,  656 , and  658 —thereby further updating the features model based on that instance (in view of the revised version of the neural network model). If not, the system may end the method  600  until a further revised version of the neural network model (and corresponding features model) is received, at which point the method  600  may be repeated. 
       FIG.  7    schematically depicts an example architecture of a robot  700 . Robot  190  and/or additional robots  192  of  FIG.  1    may implement one or more components of the example architecture of  FIG.  7   . The robot  700  includes a robot control system  760 , one or more operational components  704   a - 704   n , and one or more sensors  708   a - 708   m . The sensors  708   a - 708   m  may include, for example, vision sensors (e.g., camera(s), 3D scanners), light sensors, pressure sensors, pressure wave sensors (e.g., microphones), proximity sensors, accelerometers, gyroscopes, thermometers, barometers, and so forth. While sensors  708   a - 708   m  are depicted as being integral with robot  700 , this is not meant to be limiting. In some implementations, sensors  708   a - 708   m  may be located external to robot  700 , e.g., as standalone units. 
     Operational components  704   a - 704   n  may include, for example, one or more end effectors (e.g., grasping end effectors) and/or one or more servo motors or other actuators to effectuate movement of one or more components of the robot. For example, the robot  700  may have multiple degrees of freedom and each of the actuators may control actuation of the robot  700  within one or more of the degrees of freedom responsive to the control commands. As used herein, the term actuator encompasses a mechanical or electrical device that creates motion (e.g., a motor), in addition to any driver(s) that may be associated with the actuator and that translate received control commands into one or more signals for driving the actuator. Accordingly, providing a control command to an actuator may comprise providing the control command to a driver that translates the control command into appropriate signals for driving an electrical or mechanical device to create desired motion. 
     The control system  702  may be implemented in one or more processors, such as a CPU, GPU, and/or other controller(s) of the robot  700 . In some implementations, the robot  700  may comprise a “brain box” that may include all or aspects of the control system  702 . For example, the brain box may provide real time bursts of data to the operational components  704   a - n , with each of the real time bursts comprising a set of one or more control commands that dictate, inter alia, the features of motion (if any) for each of one or more of the operational components  704   a - n.    
     Although control system  702  is illustrated in  FIG.  7    as an integral part of the robot  700 , in some implementations, all or aspects of the control system  702  may be implemented in a component that is separate from, but in communication with, robot  700 . For example, all or aspects of control system  702  may be implemented on one or more computing devices that are in wired and/or wireless communication with the robot  700 , such as computing device  810 . 
     In some implementations, the control system  702  functionally implements and/or interfaces with one or more of the components  103  of  FIG.  1   . For example, the control system  702  may implement the features modules  121 A-N, the planning module  122 , the commands module  123 , the UI input module  124 , the UI output module  125 , the local update module  126 , the correction instance engine  130 , and/or the robot data engine  135 . Also, for example, the control system  702  may interface with (e.g., via network interface  715 ) NLP system  133 . One or more of the neural network models  150 A-N may be stored locally at the robot  700  and accessible to the control system  702 . 
       FIG.  8    is a block diagram of an example computing device  810  that may optionally be utilized to perform one or more aspects of techniques described herein. Computing device  810  typically includes at least one processor  814  which communicates with a number of peripheral devices via bus subsystem  812 . These peripheral devices may include a storage subsystem  824 , including, for example, a memory subsystem  825  and a file storage subsystem  826 , user interface output devices  820 , user interface input devices  822 , and a network interface subsystem  816 . The input and output devices allow user interaction with computing device  810 . Network interface subsystem  816  provides an interface to outside networks and is coupled to corresponding interface devices in other computing devices. 
     User interface input devices  822  may include a keyboard, pointing devices such as a mouse, trackball, touchpad, or graphics tablet, a scanner, a touchscreen incorporated into the display, audio input devices such as voice recognition systems, microphones, and/or other types of input devices. In general, use of the term “input device” is intended to include all possible types of devices and ways to input information into computing device  810  or onto a communication network. 
     User interface output devices  820  may include a display subsystem, a printer, a fax machine, or non-visual displays such as audio output devices. The display subsystem may include a cathode ray tube (CRT), a flat-panel device such as a liquid crystal display (LCD), a projection device, or some other mechanism for creating a visible image. The display subsystem may also provide non-visual display such as via audio output devices. In general, use of the term “output device” is intended to include all possible types of devices and ways to output information from computing device  810  to the user or to another machine or computing device. 
     Storage subsystem  824  stores programming and data constructs that provide the functionality of some or all of the modules described herein. For example, the storage subsystem  824  may include the logic to perform selected aspects of the methods described herein. 
     These software modules are generally executed by processor  814  alone or in combination with other processors. Memory  825  used in the storage subsystem  824  can include a number of memories including a main random access memory (RAM)  830  for storage of instructions and data during program execution and a read only memory (ROM)  832  in which fixed instructions are stored. A file storage subsystem  826  can provide persistent storage for program and data files, and may include a hard disk drive, a floppy disk drive along with associated removable media, a CD-ROM drive, an optical drive, or removable media cartridges. The modules implementing the functionality of certain implementations may be stored by file storage subsystem  826  in the storage subsystem  824 , or in other machines accessible by the processor(s)  814 . 
     Bus subsystem  812  provides a mechanism for letting the various components and subsystems of computing device  810  communicate with each other as intended. Although bus subsystem  812  is shown schematically as a single bus, alternative implementations of the bus subsystem may use multiple busses. 
     Computing device  810  can be of varying types including a workstation, server, computing cluster, blade server, server farm, or any other data processing system or computing device. Due to the ever-changing nature of computers and networks, the description of computing device  810  depicted in  FIG.  8    is intended only as a specific example for purposes of illustrating some implementations. Many other configurations of computing device  810  are possible having more or fewer components than the computing device depicted in  FIG.  8   . 
     While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all features, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual features, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.