Robot Environment 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, update, and train a robot's simulation of its external environment using sensor data and user instructions, enabling concurrent updates by multiple users and autonomous training of the robot to minimize discrepancies between the simulation and real-world data.
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 is a digital copy of the robot's external environment, allowing remote users to interact with this simulation instead of requiring complex direct control interfaces. The simulation serves as an intermediary representation that simplifies remote operation while maintaining fidelity to the actual environment.
Solution Approach 2:
The simulated world acts as an intermediary between the robot and remote users. Rather than users directly controlling the robot through complex interfaces, they interact with the simulation which then guides robot actions, reducing interface complexity and improving accessibility.
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 digital twin of the real environment, allowing robots to undergo extensive training in the simulation without physical wear. The simulation replicates real-world conditions and physics, enabling realistic training scenarios to be executed infinitely without damaging robot components.
Solution Approach 2:
The robot learns autonomous operation through preliminary training in the simulated environment before deploying to the real world. This preliminary action in simulation allows the robot to develop autonomous capabilities without incurring wear on physical components during the learning process.
3Reliability
If robots repeatedly perform physical tasks in the real world for training, then learning effectiveness improves, but component wear and operational cost increase
Solution Approach 1:
The patent creates a virtual copy of the real-world environment where robots can perform repeated training tasks without physical wear. The simulation maintains realistic physics and interactions, ensuring that learning effectiveness is preserved while eliminating component wear during training iterations.
Solution Approach 2:
The patent changes the physical parameters of the training environment by moving from physical reality to simulated virtual reality. This parameter change allows infinite repetition of training tasks without the degrading effects of physical wear, while maintaining the essential learning dynamics through accurate simulation physics.
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.