System(s) and method(s) of using behavioral cloning value approximation in training and refining robotic control policies

A method implemented by one or more processors of a robot, the method comprising:

• receiving an instance of vision data capturing an environment of the robot during performance of a robotic task by the robot, where the instance of vision data is captured via one or more vision components of the robot;
• generating an embedding based on processing the instance of vision data using an encoder model, the encoder model being a trained neural network (NN) model;
• processing the embedding using a robotic control policy to generate action output that indicates, for each of a plurality of components of the robot, a corresponding action to be performed by the component;
• processing the embedding using a failure NN model to generate failure output indicating a likelihood of the robot successfully completing the task;
• determining, based on the failure output, whether the robot will fail in performance of the robotic task;
• in response to determining that the robot will fail in performance of the robotic task:
• halting performance of the task by the robot;
• and
• in response to determining that the robot will not fail in performance of the robotic task:
• continuing performance of the task by the robot, continuing performance of the task by the robot comprising:
• causing implementation of the corresponding actions by the components of the robot.