Cloud Object Recognition Delegation for Robot Pose Detection
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Solution Overview
Problem
Robots are resource-constrained and face challenges in efficiently classifying a plethora of object types and poses due to the resource-intensive nature of object and pose recognition, which can lead to sluggish and erratic behavior.
Innovation Solution
A cloud-based object recognition engine (CORE) that delegates object type and pose detection to a plurality of targeted object recognition modules, utilizing a distributed and scalable architecture to process sensor data and provide inferences, resolving conflicts through a graph-based approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the robot stores locally all object models and data needed to classify all possible objects and poses, then the robot can perform object recognition independently, but the robot's resources are overwhelmed causing sluggish and erratic behavior
Solution Approach 1:
The patent extracts the computationally intensive object recognition processing from the resource-constrained robot and relocates it to a cloud-based server. The robot only retains lightweight client functionality to send sensor data and receive recognition results, thereby maintaining reliable object recognition capability while eliminating the performance degradation caused by local resource constraints.
Solution Approach 2:
The patent introduces an object recognition client as an intermediary layer between the robot's sensors and the cloud-based object models. This client manages the delegation of recognition tasks to multiple targeted recognition modules in the cloud, coordinating their outputs to produce final classification results. The intermediary handles the complexity of resource management and result aggregation, allowing the robot to maintain responsiveness while accessing comprehensive object recognition capabilities.
2Measurement precision
If the robot applies a large number of object models to each unclassified object, then the classification accuracy improves, but the robot's computational resources are overwhelmed
Solution Approach 1:
The patent segments the large set of object models into multiple targeted recognition modules, each specializing in specific object categories or features. Instead of applying all models simultaneously to each object (which would overwhelm resources), the system selectively delegates queries to relevant subsets of modules based on the object characteristics, thereby maintaining high classification accuracy while reducing computational load on the robot.
Solution Approach 2:
The patent implements a recognition system that can query more object models than strictly necessary (excessive action) by leveraging the cloud's unlimited resources, while the robot itself performs only partial processing locally. The object recognition client manages this excessive querying by coordinating multiple cloud-based modules, allowing the system to achieve high accuracy without the robot bearing the full computational burden.
3Device complexity
If the robot performs object recognition locally with limited resources, then the system complexity is low, but the robot cannot handle a plethora of different object types and poses
Solution Approach 1:
The patent creates a universal object recognition system where a single robot can handle a plethora of different object types and poses by leveraging cloud-based resources. The object recognition client provides multi-functionality by dynamically querying different targeted recognition modules based on the object being analyzed, allowing the robot to adapt to diverse object categories without maintaining specialized local processing capabilities for each type.
Solution Approach 2:
The patent transitions the object recognition capability from a local two-dimensional constraint (robot's limited onboard storage and processing) to a cloud-based three-dimensional space (unlimited remote storage, processing power, and model diversity). This dimensional shift allows the system to maintain simple local architecture while achieving comprehensive object type coverage through networked access to distributed recognition modules.
Data Source
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AI summary
Methods, apparatus, systems, and computer-readable media are provided for delegating object type and/or pose detection to a plurality of "targeted object recognition modules." In some implementations, a method may be provided that includes: operating an object recognition client to facilitate object recognition for a robot; receiving, by the object recognition client, sensor data indicative of an observed object in an environment; providing, by the object recognition client, to each of a plurality of remotely-hosted targeted object recognition modules, data indicative of the observed object; receiving, by the object recognition client, from one or more of the plurality of targeted object recognition modules, one or more inferences about an object type or pose of the observed object; and determining, by the object recognition client, information about the observed object, such as its object type and/or pose, based on the one or more inferences.