Robot World Simulation for Collaborative Tele-Operation Training
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Solution Overview
Problem
Current tele-operation systems for robots require complex interfaces and significant pilot attention, limiting accessibility and causing wear on robot components during training, and lack efficient methods for robots to autonomously construct and update models of their external environments.
Innovation Solution
A method and system that allows a tele-operation system to display and update a simulation of a robot's external environment based on sensor data and user instructions, enabling the robot to autonomously refine its simulation model over time, using a combination of sensor data and simulation instructions to align the virtual environment with the real one, and allowing multiple users to concurrently contribute to the simulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If tele-operation systems use elaborate and complicated interfaces with sophisticated sensors and equipment, then the robot can be controlled remotely, but the system complexity increases and accessibility is limited
Solution Approach 1:
The patent creates a simulated world that copies the robot's external environment, allowing tele-operators to interact with a virtual representation rather than requiring complex direct control interfaces. This simulation copy enables simpler interaction while maintaining control capabilities.
Solution Approach 2:
The simulated world acts as an intermediary between the tele-operator and the actual robot environment. Instead of directly controlling the robot through complex interfaces, operators interact with the simulation, which mediates the control actions and provides feedback.
2Extent of automation
If robots are trained to operate semi-autonomously or fully autonomously, then operational independence improves, but significant wear and tear occurs on robot components during training
Solution Approach 1:
The patent uses a simulated world as a copy of the real environment for training purposes. Robots can practice and develop autonomous capabilities in the virtual simulation without physical wear and tear on actual components, then transfer learned skills to real-world operation.
Solution Approach 2:
The simulation enables preliminary training and practice before actual deployment. Robots can perform repeated tasks and learn autonomous operation in the virtual environment beforehand, reducing the need for extensive physical training that would cause component wear.
3Productivity
If robots repeatedly perform physical tasks in the real world for training, then autonomous operation capability improves, but component wear and tear increases significantly
Solution Approach 1:
The patent creates a virtual copy of the training environment where robots can perform repeated tasks indefinitely without physical degradation. This simulation copy allows unlimited training iterations that would be impossible in the physical world due to component wear.
Solution Approach 2:
The simulation system provides self-service training capabilities where the robot can practice autonomously without requiring physical resources. The virtual environment automatically resets and replenishes, allowing continuous training without the lifespan limitations of physical components.
Data Source
AI summary
Systems, computer program products, and methods for constructing models and simulations of real-world environments are described. A robot employs various sensors to collect data from its environment and provides this data to a tele-operation system. Any number of tele-artists may access the tele-operation system and use the robot sensor data to collaboratively construct a simulated scene representative of the robot's environment. The tele-artists may continue to update the simulation in real-time as the robot explores its environment and provides more sensor data. The robot may use the simulation in support of fundamental operations through its cognitive architecture, such as action planning and hypothesis generation.An artificial intelligence controller of the robot may monitor the adaptations made to the simulation by the tele-artists in response to the sensor data in order to learn (e.g., via reinforcement learning) how to autonomously generate and update its own simulation based on its own sensor data.