Mitsubishi Electric Research Laboratories, Inc. (20240288870). Method and System for Generating a Sequence of Actions for Controlling a Robot simplified abstract

Method and System for Generating a Sequence of Actions for Controlling a Robot

Organization Name

Mitsubishi Electric Research Laboratories, Inc.

Inventor(s)

Chiori Hori of Lexington MA (US)

Jonathan Le Roux of Arlington MA (US)

Devesh Jha of Cambridge MA (US)

Siddarth Jain of Cambridge MA (US)

Radu Ioan Corcodel of Cambridge MA (US)

Diego Romeres of Boston MA (US)

Puyuang Peng of Austin TX (US)

Xinyu Liu of Province RI (US)

David Harwath of Austin TX (US)

Method and System for Generating a Sequence of Actions for Controlling a Robot - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240288870 titled 'Method and System for Generating a Sequence of Actions for Controlling a Robot

Simplified Explanation:

This patent application describes a method, system, and computer program product for using a neural network with an action sequence decoder to generate a sequence of actions for a robot to perform a task based on recordings of humans performing similar tasks.

• The method involves collecting recordings and captions describing scenes in the recordings.
• Feature data is extracted from the recordings, including video, audio, and text transcription.
• The extracted feature data is encoded to produce a sequence of encoded features.
• The action sequence decoder generates a sequence of actions for the robot based on the encoded features and the semantic meaning of the captions.

Key Features and Innovation:

• Utilizes a neural network with an action sequence decoder for task performance by a robot.
• Incorporates recordings of humans performing tasks to generate action sequences.
• Extracts and encodes feature data from recordings to guide the robot's actions.
• Matches semantic meaning of captions with encoded features to determine robot actions.

Potential Applications:

• Robotics automation in various industries.
• Assistive technology for individuals with disabilities.
• Virtual reality and augmented reality applications.
• Training and education in robotics and artificial intelligence.

Problems Solved:

• Enhances efficiency and accuracy of robot task performance.
• Bridges the gap between human demonstrations and robot actions.
• Improves human-robot interaction and collaboration.

Benefits:

• Streamlines robot programming and task execution.
• Increases adaptability and versatility of robots.
• Enhances user experience and satisfaction.

Commercial Applications:

Artificial Intelligence in Robotics: Enhancing Task Performance and Efficiency

Questions about Artificial Intelligence in Robotics:

1. How does this technology improve the interaction between humans and robots? 2. What are the potential limitations or challenges of implementing this neural network system in real-world scenarios?

Original Abstract Submitted

a method, a system and a computer program product are provided for applying a neural network including an action sequence decoder for generating an action sequence for a robot to perform a task. the neural network is applied to generate the action sequence based on recordings demonstrating humans performing tasks. in an example, the method comprises collecting a recording and a sequence of captions describing scenes in the recording; extracting feature data from the recording; encoding the extracted feature data to produce a sequence of encoded features; and applying the action sequence decoder to produce a sequence of actions for the robot based on the sequence of encoded features having a semantic meaning corresponding to a semantic meaning of the sequence of captions. the feature data includes features of a video signal, an audio signal, and/or text transcription capturing a performance of the task.