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 to generate actions for the agent.

Receiving a natural language text sequence describing a task for the agent.
Generating an encoded representation of the text sequence.
Processing observation images of the environment to generate actions for the agent.
Selecting actions based on the generated policy output.
Causing the agent to perform the selected actions.

Key Features and Innovation

Use of natural language text sequences to control agent actions.
Processing observation images to generate actions for the agent.
Integration of policy outputs to select agent actions efficiently.

Potential Applications

This technology can be applied in various fields such as robotics, automation, virtual assistants, and gaming.

Problems Solved

Efficiently controlling agent actions based on natural language instructions. Enhancing the interaction between agents and their environments.

Benefits

Improved efficiency in task performance. Enhanced user experience in controlling agents. Increased adaptability of agents to different environments.

Commercial Applications

Potential commercial uses include robotics automation systems, virtual assistant technologies, and gaming platforms.

Prior Art

Researchers can explore prior art related to natural language processing in robotics and AI systems.

Frequently Updated Research

Stay updated on advancements in natural language processing for agent control systems.

Questions about Agent Control Technology

How does this technology improve user interaction with agents?

This technology enhances user experience by allowing control of agents through natural language instructions, making interactions more intuitive and efficient.

What are the potential limitations of using natural language text sequences to control agents?

One potential limitation could be the complexity of interpreting and processing a wide range of natural language instructions accurately.

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.

Google llc (20240189994). REAL-WORLD ROBOT CONTROL USING TRANSFORMER NEURAL NETWORKS simplified abstract

Contents

REAL-WORLD ROBOT CONTROL USING TRANSFORMER NEURAL NETWORKS

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Inventor(s)