A method (500, 600) implemented by one or more processors associated with a robot, the robot associated with a vision sensor configured to capture a portion of an environment of the robot, the method comprising: determining that an object in an environment of the robot is not recognizable, wherein determining that the object is not recognizable comprises: identifying object vision sensor data generated by the vision sensor, the object vision sensor data capturing a portion of an environment of the robot; and determining, based on application of the object vision sensor data to one or more object models or machine learning models, that the object in the environment is not recognizable based on the object models or the machine learning models; in response to determining the object is not recognizable, causing the vision sensor to capture additional object vision sensor data, the capturing comprising moving at least one of the vision sensor and the object to capture, in the additional object vision sensor data, the object from a plurality of vantages; generating an object model of the object based on the additional object vision sensor data; generating a plurality of rendered images based on the object model, wherein the rendered images capture the object model at a plurality of different poses relative to viewpoints of the rendered images, and wherein generating the rendered images based on the object model comprises: rendering a first image that renders the object model and that includes first additional content and rendering a second image that renders the object model and that includes second additional content that is distinct from the first additional content; generating training examples that each include a corresponding one of the rendered images as training example input and that each include an indication of the object as training example output; and training a machine learning model based on the training examples, wherein the trained machine learning model enables one or both of: detection of the object based on subsequent application of further vision sensor data to the trained machine learning model and estimation of a pose of the object based on subsequent application of the further vision sensor data to the trained machine learning model. determining that an object in an environment of the robot is not recognizable, wherein determining that the object is not recognizable comprises: identifying object vision sensor data generated by the vision sensor, the object vision sensor data capturing a portion of an environment of the robot; and determining, based on application of the object vision sensor data to one or more object models or machine learning models, that the object in the environment is not recognizable based on the object models or the machine learning models; identifying object vision sensor data generated by the vision sensor, the object vision sensor data capturing a portion of an environment of the robot; and determining, based on application of the object vision sensor data to one or more object models or machine learning models, that the object in the environment is not recognizable based on the object models or the machine learning models; in response to determining the object is not recognizable, causing the vision sensor to capture additional object vision sensor data, the capturing comprising moving at least one of the vision sensor and the object to capture, in the additional object vision sensor data, the object from a plurality of vantages; generating an object model of the object based on the additional object vision sensor data; generating a plurality of rendered images based on the object model, wherein the rendered images capture the object model at a plurality of different poses relative to viewpoints of the rendered images, and wherein generating the rendered images based on the object model comprises: rendering a first image that renders the object model and that includes first additional content and rendering a second image that renders the object model and that includes second additional content that is distinct from the first additional content; generating training examples that each include a corresponding one of the rendered images as training example input and that each include an indication of the object as training example output; and training a machine learning model based on the training examples, wherein the trained machine learning model enables one or both of: detection of the object based on subsequent application of further vision sensor data to the trained machine learning model and estimation of a pose of the object based on subsequent application of the further vision sensor data to the trained machine learning model.