18601159. CONTROLLING A ROBOT BASED ON FREE-FORM NATURAL LANGUAGE INPUT simplified abstract (GOOGLE LLC)

CONTROLLING A ROBOT BASED ON FREE-FORM NATURAL LANGUAGE INPUT

Organization Name

GOOGLE LLC

Inventor(s)

Pararth Shah of Sunnyvale CA (US)

Dilek Hakkani-tur of Los Altos CA (US)

Juliana Kew of San Francisco CA (US)

Marek Fiser of Mountain View CA (US)

Aleksandra Faust of Palo Alto CA (US)

CONTROLLING A ROBOT BASED ON FREE-FORM NATURAL LANGUAGE INPUT - A simplified explanation of the abstract

This abstract first appeared for US patent application 18601159 titled 'CONTROLLING A ROBOT BASED ON FREE-FORM NATURAL LANGUAGE INPUT

The abstract describes the use of deep reinforcement learning to train a model for determining robotic actions at different time steps, based on sensor data and natural language input from a user.

• Deep reinforcement learning is used to train a model for robotic task completion.
• The model can determine robotic actions based on current sensor data and user-provided natural language input.
• Natural language input can direct the robot to accomplish specific tasks, with reference to intermediary steps if needed.
• For example, the robot can be directed to navigate to a landmark with reference to other landmarks along the way.

• • Potential Applications:**

- Robotics automation in various industries - Assistive technology for individuals with disabilities - Autonomous vehicles and drones

• • Problems Solved:**

- Enhancing robotic task completion efficiency - Improving human-robot interaction through natural language input - Enabling robots to follow complex instructions

• • Benefits:**

- Increased productivity and accuracy in robotic tasks - Enhanced user experience through natural language communication - Potential for autonomous decision-making in robots

• • Commercial Applications:**

Title: "Enhancing Robotic Task Completion with Deep Reinforcement Learning" This technology can be applied in industries such as manufacturing, healthcare, and logistics for automating repetitive tasks, improving efficiency, and reducing human error. The market implications include increased adoption of robotic systems for various applications.

• • Prior Art:**

Research on deep reinforcement learning in robotics and natural language processing can provide insights into prior art related to this technology.

• • Frequently Updated Research:**

Stay updated on advancements in deep reinforcement learning, robotics, and natural language processing to understand the latest developments in this field.

• • Questions about Deep Reinforcement Learning in Robotics:**

1. How does deep reinforcement learning improve robotic task completion efficiency? 2. What are the key challenges in integrating natural language input with robotic actions?

Original Abstract Submitted

Implementations relate to using deep reinforcement learning to train a model that can be utilized, at each of a plurality of time steps, to determine a corresponding robotic action for completing a robotic task. Implementations additionally or alternatively relate to utilization of such a model in controlling a robot. The robotic action determined at a given time step utilizing such a model can be based on: current sensor data associated with the robot for the given time step, and free-form natural language input provided by a user. The free-form natural language input can direct the robot to accomplish a particular task, optionally with reference to one or more intermediary steps for accomplishing the particular task. For example, the free-form natural language input can direct the robot to navigate to a particular landmark, with reference to one or more intermediary landmarks to be encountered in navigating to the particular landmark.