System(s) and method(s) of using imitation learning in training and refining robotic control policies

A method implemented using one or more processors, the method comprising:

• receiving, from one or more vision components of a robot, an instance of vision data capturing an environment of the robot, the instance of the vision data being captured during performance of a robotic task by the robot;
• processing, using an intermediate portion of a robotic control policy, the instance of the vision data to generate an intermediate representation of the instance of the vision data;
• processing, using a first control head of the robotic control policy, the intermediate representation of the instance of the vision data to generate, for an action to be performed by the robot in furtherance of the robotic task, a corresponding first set of values for a first portion of control of a component of the robot;
• processing, using a second control head of the robotic control policy, the intermediate representation of the instance of the vision data to generate, for the action, a corresponding second set of values for a second portion of control of the component of the robot;
• receiving, from a user of a computing device, user input that intervenes with performance of the robotic task, the user input being received via the computing device or an additional computing device;
• and
• causing the robotic control policy to be updated based on the user input.

A method implemented using one or more processors, the method comprising:

• receiving, from one or more vision components of a robot, an instance of vision data capturing an environment of the robot, the instance of the vision data being captured during performance of a robotic task by the robot;
• processing, using a robotic control policy, the instance of the vision data to generate a sequence of actions to be performed by the robot during the robotic task, the sequence of actions including an initial action to be performed by the robot in furtherance of the robotic task and a plurality of predicted actions that are predicted to follow the initial action, wherein processing the instance of the vision data to generate the sequence of actions using the robotic control policy comprises:
• processing, using an intermediate portion of the robotic control policy, the instance of the vision data to generate an intermediate representation of the instance of the vision data;
• processing, using a first control head of the robotic control policy, the intermediate representation of the instance of the vision data to generate, for each action included the sequence of actions, a corresponding first set of values for a first component of the robot;
• and
• processing, using a second control head of the robotic control policy, the intermediate representation of the instance of the vision data to generate, for each action included the sequence of actions, a corresponding second set of values for a second component of the robot;
• determining, based on processing the instance of the vision data using the robotic control policy, whether the robot will fail in performance of the robotic task;
• and
• in response to determining that the robot will fail in performance of the robotic task:
• causing a prompt to be rendered via an interface of a computing device or the robot, the prompt requesting a user of the computing device intervene in performance of the robotic task;
• receiving, from a user of the computing device, and based on the prompt, user input that intervenes with performance of the robotic task, the user input being received via an input device of the computing device or an additional computing device;
• and
• causing the robotic control policy to be updated based on the user input.