Robot Object Embedding Cache for Fleet-Wide Learning
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Solution Overview
Problem
Existing robot systems face challenges in efficiently learning and sharing new information, particularly in recognizing new objects, due to high computational demands and the need for extensive re-training of machine learning models.
Innovation Solution
Implementing a local cache in robots to store learned information as discrete representations, allowing for near-instantaneous learning from user feedback or other sources, and enabling sharing of this information among robots to facilitate rapid fleet-wide learning without the need for re-training machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If robots re-train machine learning models to learn new information, then learning accuracy is improved, but learning time and computational resources increase significantly
Solution Approach 1:
The patent segments the learning process into two distinct components: (1) a pre-trained machine learning model that provides foundational object recognition capabilities, and (2) a local cache that stores discrete representations of newly learned objects. This segmentation allows robots to quickly acquire new object knowledge through cache storage and retrieval without triggering full model re-training, thereby reducing learning time while maintaining accuracy through the combined system.
Solution Approach 2:
The patent implements preliminary action by pre-training the machine learning model on a comprehensive dataset of objects before deployment. This preliminary training equips the model with robust general object recognition skills, allowing it to handle novel objects through the cache mechanism without requiring time-consuming re-training when new objects are encountered.
2Productivity
If robots share learned information frequently across the fleet, then fleet-wide learning speed is improved, but communication overhead and data transmission time increase
Solution Approach 1:
The patent uses copying by distributing discrete object representations (embeddings) from the cache to multiple robots across the fleet. Instead of sharing entire datasets or re-training models, each robot receives compact copies of learned object representations, enabling rapid knowledge propagation with minimal communication overhead and data transmission time.
3Speed
If robots store discrete representations in local cache, then learning speed is improved, but memory usage increases
Solution Approach 1:
The patent applies local quality by implementing a local cache specific to each robot that stores only the discrete representations of objects relevant to that robot's experience and task requirements. This localized approach ensures that memory resources are used efficiently, storing only necessary information rather than maintaining complete object databases, thereby achieving fast local learning without excessive memory consumption.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for sharing learned information among robots. In some implementations, a robot obtains sensor data indicating characteristics of an object. The robot determines a classification for the object and generates an embedding for the object using a machine learning model stored by the robot. The robot stores the generated embedding and data indicating the classification for the object. The robot sends the generated embedding and the data indicating the classification to a server system. The robot receives, from the server system, an embedding generated by a second robot and a corresponding classification. The robot stores the received embedding and the corresponding classification in the local cache of the robot. The robot may then use the information in the cache to identify objects.