Google llc (20240249109). CONTROLLING A ROBOT BASED ON FREE-FORM NATURAL LANGUAGE INPUT simplified abstract

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 20240249109 titled 'CONTROLLING A ROBOT BASED ON FREE-FORM NATURAL LANGUAGE INPUT

The patent application relates to using deep reinforcement learning to train a model for determining robotic actions at each time step for completing tasks, as well as controlling a robot based on this model.

• Utilizes deep reinforcement learning to train a model for robotic actions
• Model considers current sensor data and natural language input from a user
• Natural language input directs the robot to accomplish specific tasks, potentially with intermediary steps
• Example tasks include navigating to landmarks with reference to intermediary landmarks

Potential Applications: - Robotics automation in various industries - Assistive robots for individuals with disabilities - Autonomous vehicles for navigation and decision-making

Problems Solved: - Enhancing robot autonomy and decision-making capabilities - Improving human-robot interaction through natural language input

Benefits: - Increased efficiency in task completion - Enhanced user experience with robots - Potential for complex task execution by robots

Commercial Applications: Title: "Enhancing Robot Autonomy with Deep Reinforcement Learning" This technology can be applied in industries such as manufacturing, healthcare, and logistics for automating repetitive tasks, improving productivity, and reducing human error.

Questions about the technology: 1. How does the use of deep reinforcement learning improve the decision-making capabilities of robots? - Deep reinforcement learning allows robots to learn from experience and optimize their actions over time, leading to more efficient task completion.

2. What are the advantages of incorporating natural language input from users in controlling robots? - Natural language input enables users to easily communicate tasks to robots, making human-robot interaction more intuitive and user-friendly.

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.