Robot Learning Control for Autonomous Task Adaptation

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Solution Overview

Problem

Existing robots require significant time and effort from programmers to develop instructions for specific environments, limiting their ability to operate in dynamic or new settings and causing operational inefficiencies.

Innovation Solution

Employing an AI policy model that identifies tasks based on initial parameters and generates output data using an AI model, allowing robots to autonomously adapt to new or different environments by training on input data to perform operations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional programming methods are used to develop robot instructions for specific environments, then the robot can perform tasks reliably, but significant time and effort from programmers is required

Engineering Contradiction:

Improvetask execution reliabilityVSAvoidprogrammer time and effort

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The robot performs self-learning by autonomously observing human demonstrations and generating its own task policies through reinforcement learning, eliminating the need for programmers to manually code instructions for each environment

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system pre-trains the robot in simulated environments before deployment, allowing it to learn fundamental tasks and adapt to new settings in advance, reducing on-site programming requirements

Inventive Principle:

Principle #10Preliminary action

2Adaptability or versatility

If traditional programming methods are used to create robot instructions, then the robot operates in controlled environments, but it cannot adapt to dynamic or new settings

Engineering Contradiction:

Improveenvironmental adaptabilityVSAvoidprogramming complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The robot's task policies are made dynamic through reinforcement learning, allowing them to adapt continuously to changing environments and new settings rather than relying on static pre-programmed instructions

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes the training parameters by exposing the robot to diverse simulated environments with varying conditions, enabling it to learn generalizable policies that adapt to real-world variability

Inventive Principle:

Principle #35Parameter changes

3Manufacturing precision

If extensive programmer intervention is required to develop robot instructions, then the robot can perform tasks accurately, but operational efficiency decreases

Engineering Contradiction:

Improvetask execution precisionVSAvoidoperational efficiency

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The patent replaces manual programming mechanics with automated machine learning systems, where algorithms automatically learn task policies from data rather than requiring human programmers to write code

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20260029801A1Methods and systems for robot learning and controlling a robot

Publication Date: 2026.01.29 COLLABORATIVE ROBOTICS

AI summary

A method may include obtaining input data corresponding to a robot. The method may also include generating, using an artificial intelligence (AI) model, output data based on the input data. The output data may be representative of a state of the robot. In addition, the method may include identifying, using an AI policy model, a set of tasks to be performed by the robot based on the output data. The set of tasks may involve movement of the robot associated with the state of the robot to perform an operation. The method may include causing the robot to autonomously perform the set of tasks to complete the operation.