Robot Teleoperation Feedback for Faster Reinforcement Learning

Solution Overview

Problem

Training robots using reinforcement learning is inefficient due to the need for numerous trial-and-error attempts, which can be time-consuming and may cause unnecessary wear and tear, as teleoperators often lack insight into the robot's state and technical constraints.

Innovation Solution

A teleoperation system provides feedback to teleoperators through sensor information, visual guides, and haptic cues, allowing for precise control and efficient data collection, which is then used to train a reinforcement-learning model capable of controlling robots without human intervention.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If reinforcement learning is used to train robots through trial-and-error attempts, then robots can learn to complete tasks autonomously, but the training process becomes time-consuming and causes unnecessary wear and tear on robot components

Engineering Contradiction:

Improveautonomous task completionVSAvoidtraining time

Core Design Contradiction:

Extent of automationVSLoss of time

Solution Approach 1:

The system performs preliminary action by having a teleoperator demonstrate the task once, recording the state-action pairs. This preliminary demonstration provides the foundation training data that eliminates the need for extensive trial-and-error attempts, allowing the robot to learn the task quickly without time-consuming iterations.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system uses copying by recording the teleoperator's actions and the robot's states during demonstration, then using these recorded data to train the reinforcement learning model. This copying approach replaces the need for the robot to physically attempt the task repeatedly, significantly reducing training time and wear on components.

Inventive Principle:

Principle #26Copying

2Extent of automation

If reinforcement learning is used to train robots through trial-and-error attempts, then robots can learn to complete tasks autonomously, but the training process causes unnecessary wear and tear on robot components

Engineering Contradiction:

Improveautonomous task completionVSAvoidwear and tear on components

Core Design Contradiction:

Extent of automationVSObject-affected harmful factors

Solution Approach 1:

The system performs preliminary action by having a teleoperator demonstrate the task once, recording the state-action pairs. This preliminary demonstration provides the foundation training data that eliminates the need for extensive trial-and-error attempts, allowing the robot to learn the task quickly without time-consuming iterations.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system uses copying by recording the teleoperator's actions and the robot's states during demonstration, then using these recorded data to train the reinforcement learning model. This copying approach replaces the need for the robot to physically attempt the task repeatedly, significantly reducing training time and wear on components.

Inventive Principle:

Principle #26Copying

3Ease of operation

If teleoperators control robots without feedback on robot state and constraints, then teleoperation is simple to operate, but the training data quality decreases leading to more errors

Engineering Contradiction:

Improveteleoperation simplicityVSAvoidtraining data accuracy

Core Design Contradiction:

Ease of operationVSManufacturing precision

Solution Approach 1:

The system implements feedback by providing the teleoperator with information about the robot's current state, sensor readings, and technical constraints during teleoperation. This feedback enables the teleoperator to make informed decisions and collect high-quality training data that reflects both task requirements and operational constraints, improving training accuracy without complicating the interface.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system uses an intermediary approach by introducing a feedback mechanism that mediates between the teleoperator's control inputs and the robot's actions. This intermediary layer provides contextual information about robot state and constraints to the teleoperator, enabling more accurate training data collection while maintaining ease of operation through the existing teleoperation interface.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12561550B2Teleoperation for training of robots using machine learning

Publication Date: 2026.02.24 SANCTUARY COGNITIVE SYST CORP

AI summary

Methods and systems for using a teleoperation system to train a robot to perform tasks using machine learning are described herein. A teleoperation system may be used to record actions of a robot as used by a human teleoperator. The teleoperation system may provide a teleoperator insight into the state of the robot and may provide feedback to the teleoperator allowing the teleoperator to feel what the robot is feeling. For example, sensor information from the robot may be sent to the teleoperation system and output to the teleoperator in various forms including vibrations, video, visual cues, or sound. The teleoperation system may output visual guides to the teleoperator so that the teleoperator may know how to control the robot to complete a task in a desired manner.