18539171. REAL-WORLD ROBOT CONTROL USING TRANSFORMER NEURAL NETWORKS simplified abstract (Google LLC)

REAL-WORLD ROBOT CONTROL USING TRANSFORMER NEURAL NETWORKS

Organization Name

Google LLC

Inventor(s)

Keerthana P G of San Francisco CA (US)

Karol Hausman of San Francisco CA (US)

Julian Ibarz of Sunnyvale CA (US)

Brian Ichter of Brooklyn NY (US)

Alexander Irpan of Palo Alto CA (US)

Dmitry Kalashnikov of Fair Lawn NJ (US)

Yao Lu of Palo Alto CA (US)

Kanury Kanishka Rao of Santa Clara CA (US)

Michael Sahngwon Ryoo of Mountain View CA (US)

Austin Charles Stone of San Francisco CA (US)

Teddey Ming Xiao of Mountain View CA (US)

Quan Ho Vuong of Palo Alto CA (US)

Sumedh Anand Sontakke of Los Angeles CA (US)

REAL-WORLD ROBOT CONTROL USING TRANSFORMER NEURAL NETWORKS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18539171 titled 'REAL-WORLD ROBOT CONTROL USING TRANSFORMER NEURAL NETWORKS

The patent application describes methods, systems, and apparatus for controlling an agent interacting with an environment using natural language text sequences.

• Receiving a natural language text sequence that describes a task for the agent.
• Generating an encoded representation of the text sequence.
• Processing observation images of the environment at each time step.
• Generating a sequence of input tokens based on the observation images.
• Using a policy output to define actions for the agent in response to the images.
• Selecting actions for the agent based on the policy output.
• Causing the agent to perform the selected actions.

Potential Applications: - Robotics - Autonomous vehicles - Virtual assistants

Problems Solved: - Streamlining communication between humans and agents - Enhancing the efficiency of task performance in complex environments

Benefits: - Improved task execution accuracy - Enhanced user experience in interacting with agents - Increased automation capabilities in various industries

Commercial Applications: Title: "Enhanced Agent Control System for Robotics and Automation" This technology can be applied in industries such as manufacturing, logistics, and customer service to optimize operations and improve productivity.

Prior Art: Researchers can explore existing patents related to natural language processing, robotics, and artificial intelligence to understand the evolution of similar technologies.

Frequently Updated Research: Stay updated on advancements in natural language processing, reinforcement learning, and computer vision to enhance the capabilities of agent control systems.

Questions about Agent Control Systems: 1. How does this technology improve the efficiency of task performance in complex environments? 2. What are the potential challenges in implementing natural language processing for agent control systems?

Original Abstract Submitted

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling an agent interacting with an environment. In one aspect, a method comprises: receiving a natural language text sequence that characterizes a task to be performed by the agent in the environment; generating an encoded representation of the natural language text sequence; and at each of a plurality of time steps: obtaining an observation image characterizing a state of the environment at the time step; processing the observation image to generate an encoded representation of the observation image; generating a sequence of input tokens; processing the sequence of input tokens to generate a policy output that defines an action to be performed by the agent in response to the observation image; selecting an action to be performed by the agent using the policy output; and causing the agent to perform the selected action.