Robot Object Search Using Learned Environmental Knowledge
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Solution Overview
Problem
Conventional robots are inefficient in locating and identifying objects that are not directly in view, often requiring time-consuming operations like SLAM to exhaustively map environments, which consumes resources and can be disruptive.
Innovation Solution
A method using machine learning models, such as convolutional neural networks, to generate posteriori knowledge about object locations, allowing robots to quickly identify potential locations of objects of interest by analyzing visual frames and adjusting their position or pose for optimal viewing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If robots perform exhaustive mapping operations (SLAM) to locate objects, then they can identify objects not directly in view, but they consume excessive resources (power, processing cycles, memory) and time
Solution Approach 1:
The system performs preliminary actions by training machine learning models with a posteriori knowledge about object locations in advance. This pre-computed knowledge allows the robot to quickly query likely object locations without performing exhaustive SLAM operations at runtime, thus resolving the contradiction between accurate object location identification and time consumption.
Solution Approach 2:
The patent introduces machine learning models as an intermediary between the robot's vision sensors and the object location determination process. These models act as a mediator that uses learned patterns from training data to predict object locations, replacing the direct but resource-intensive SLAM approach and enabling faster object location with reduced computational burden during operation.
2Measurement precision
If robots perform exhaustive mapping operations (SLAM) to locate objects, then they can identify objects not directly in view, but they expend excessive resources (power, processing cycles, memory)
Solution Approach 1:
The system performs preliminary actions by training machine learning models with a posteriori knowledge about object locations in advance. This pre-computed knowledge allows the robot to quickly query likely object locations without performing exhaustive SLAM operations at runtime, thus resolving the contradiction between accurate object location identification and power consumption.
Solution Approach 2:
The patent introduces machine learning models as an intermediary between the robot's vision sensors and the object location determination process. These models act as a mediator that uses learned patterns from training data to predict object locations, replacing the direct but resource-intensive SLAM approach and enabling faster object location with reduced computational burden during operation.
3Measurement precision
If robots perform exhaustive mapping operations to locate objects, then they can identify objects not directly in view, but the operations are disruptive in home or business environments
Solution Approach 1:
The system performs preliminary actions by training machine learning models with a posteriori knowledge about object locations in advance. This pre-computed knowledge allows the robot to quickly query likely object locations without performing exhaustive SLAM operations at runtime, thus resolving the contradiction between accurate object location identification and environmental disruption.
4Adaptability or versatility
If robots use conventional methods to locate objects, then they can find objects anywhere in the environment, but the search process is time-consuming and inefficient
Solution Approach 1:
The patent applies local quality by using machine learning models to identify specific regions of interest in the environment where objects are likely to be located, based on learned patterns from training data. Instead of uniformly searching the entire environment, the system focuses computational resources on localized areas with higher probability of containing the target object, thus improving object location speed while maintaining adaptability.
Solution Approach 2:
The system changes parameters by using probability thresholds and confidence levels to dynamically adjust the search strategy. When the machine learning model predicts high-probability locations, the robot can directly navigate to those areas; when confidence is lower, it can expand the search area. This parameter-based approach balances object location coverage and speed adaptively.
Data Source
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Figure 3A~3B
AI summary
Techniques described herein relate to generating a posteriori knowledge about where objects are typically located within environments to improve object location. In various implementations, output from vision sensor(s) of a robot may include visual frame(s) that capture at least a portion of an environment in which a robot operates/will operate. The visual frame(s) may be applied as input across a machine learning model to generate output that identifies potential location(s) of an object of interest. The robot's position/pose may be altered based on the output to relocate one or more of the vision sensors. One or more subsequent visual frames that capture at least a not-previously-captured portion of the environment may be applied as input across the machine learning model to generate subsequent output identifying the object of interest. The robot may perform task(s) that relate to the object of interest.