Predictive Robotic Control for Adaptive User-Guided Training

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Solution Overview

Problem

Existing robotic devices face challenges in adapting to changes in their model or environment, as programming is costly and remote control requires human operators, which can be inadequate in dynamic situations, and may require frequent code changes due to unexpected obstacles or environment changes.

Innovation Solution

A computerized controller apparatus that uses processors to execute modules for training robotic devices, determining control signals based on inputs and user interactions, allowing the robotic device to perform actions without explicit user input during subsequent trials, utilizing a predictor sub-module to analyze sensory signals and generate predicted control outputs, and a combiner sub-module to combine predicted outputs with user inputs for improved performance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If robotic devices are programmed to perform desired functionality, then the robot can execute specific tasks, but programming is costly and requires frequent code changes when the robot model or environment changes

Engineering Contradiction:

Improveadaptability to model or environment changesVSAvoidprogramming complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The robotic device performs self-training by autonomously interacting with the environment, collecting sensory data, and updating its own control policies through machine learning algorithms. This eliminates the need for external programmers to manually reprogram the robot when environmental changes occur, as the robot adapts itself through continuous learning from its experiences.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary training actions by exposing the robotic device to a wide variety of environmental scenarios and disturbances before actual deployment. Through this pre-training phase, the robot learns robust control policies that can handle unexpected changes, reducing the need for subsequent programming modifications when environmental conditions change.

Inventive Principle:

Principle #10Preliminary action

2Adaptability or versatility

If robotic devices are remotely controlled by humans, then human operators can make decisions, but remote control requires human operators and may be inadequate when dynamics change rapidly

Engineering Contradiction:

Improveresponse to dynamic environment changesVSAvoidoperational simplicity

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The patent replaces the mechanical control system (human operator remotely controlling the robot) with an intelligent control system based on machine learning and neural networks. The robotic device autonomously processes sensory inputs and generates control actions through trained policies, eliminating the need for human operators while maintaining adaptive decision-making capabilities through algorithmic intelligence.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The control system is designed to be dynamic and adaptive rather than static. The robotic device continuously updates its control policies based on real-time environmental feedback and learned experiences, allowing it to rapidly adapt to changing dynamics without human intervention. The system's behavior evolves over time as it learns from new situations.

Inventive Principle:

Principle #15Dynamics

3Adaptability or versatility

If robotic devices use exploration to learn operation, then the robot can adapt to new situations, but learning through exploration requires extensive training time and resources

Engineering Contradiction:

Improveability to learn new operationsVSAvoidtraining time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11224971B2Predictive robotic controller apparatus and methods

Publication Date: 2022.01.18 BRAIN CORP

AI summary

Robotic devices may be trained by a user guiding the robot along target action trajectory using an input signal. A robotic device may comprise an adaptive controller configured to generate control signal based on one or more of the user guidance, sensory input, performance measure, and/or other information. Training may comprise a plurality of trials, wherein for a given context the user and the robot's controller may collaborate to develop an association between the context and the target action. Upon developing the association, the adaptive controller may be capable of generating the control signal and/or an action indication prior and/or in lieu of user input. The predictive control functionality attained by the controller may enable autonomous operation of robotic devices obviating a need for continuing user guidance.