Robot Task Selection Using HIVE Training for Autonomous Problem Remedy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing robot systems struggle to effectively address unforeseen problems in inhospitable environments, such as extraterrestrial or inhospitable Earth locations, due to the lack of real-time human intervention and insufficient adaptive learning capabilities.
Innovation Solution
A robot intelligence engine that utilizes highly immersive virtual environments (HIVEs) to train robots, simulates potential robot tasks, and employs machine learning algorithms to select and prioritize tasks for deployed robots to remedy detected problems, incorporating unsupervised learning to adapt to new situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If robots are deployed in inhospitable environments without real-time human intervention, then operational autonomy is improved, but the ability to effectively address unforeseen problems deteriorates
Solution Approach 1:
The system performs preliminary action by training robots in advance using highly immersive virtual environments (HIVEs) that simulate various problem scenarios. The robot intelligence engine pre-learns potential problems and solutions through machine learning algorithms before deployment, enabling the robot to autonomously address unforeseen issues without real-time human intervention.
Solution Approach 2:
The system implements self-service through unsupervised learning capabilities where the robot intelligence engine continuously learns from new data and experiences encountered during operation. The robot autonomously adapts its knowledge base and problem-solving strategies without requiring external retraining or human intervention, enabling it to handle previously unseen problems independently.
2Ease of manufacture
If traditional robot learning methods are used, then development simplicity is maintained, but the speed and effectiveness of adapting to new environments deteriorates
Solution Approach 1:
The system uses copying by creating highly realistic virtual copies of real-world environments, objects, and problem scenarios within HIVEs. These virtual replicas allow robots to practice and learn problem-solving skills in simulated conditions that closely mirror actual deployment environments, accelerating adaptation without requiring physical presence in hazardous locations during training.
Solution Approach 2:
The system applies parameter changes by dynamically adjusting environmental parameters, problem scenarios, and task complexities within the HIVE training environments. The robot intelligence engine modifies training parameters based on performance metrics and emerging challenges, enabling continuous optimization of learning effectiveness and acceleration of adaptation to new environments.
Data Source
AI summary
A robot intelligence engine receives highly immersive virtual environment (HIVE) data characterizing a set of robot tasks executed by a test robot in a HIVE, wherein the robot tasks of the set of robot tasks include a robot skill. The robot intelligence engine receives sensor data from a problem detecting robot deployed in an environment of operation that characterizes conditions corresponding to a detected problem and searches the set of robot tasks to identify a subset of the robot tasks that are potentially employable to remedy the detected problem. The robot intelligence engine simulates the subset of robot tasks to determine a likelihood of success for the subset of robot tasks. The simulation generates a set of unsupervised robot tasks that are potentially employable to remedy the detected problem. The robot intelligence engine selects one of the subset of robot tasks or one of the unsupervised robot tasks.