Robot Grasp Learning Using Failed Attempts and Simulation
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Solution Overview
Problem
Robots often fail to grasp objects securely, either dropping them or applying too much force, and may disturb nearby objects or grasp incorrect items, especially in complex environments, due to limitations in their grasp models.
Innovation Solution
A fleet of robots shares data on failed grasp attempts with a remote server system, which analyzes and updates a neural network grasp model using retrospective analysis and simulation, distributing the improved model to enhance the robots' grasping abilities without individual training on each object.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If robots train grasp models individually on each object, then the model accuracy for specific objects improves, but the time and computational resources required increase significantly
Solution Approach 1:
The patent combines grasp attempt data from multiple robots into a centralized training dataset. Instead of each robot training individually on limited objects, the system merges data across the fleet to create a comprehensive dataset that is shared for model training, reducing redundant training time while improving model accuracy through diverse object samples
Solution Approach 2:
The system performs preliminary data collection and preprocessing by gathering grasp attempts from multiple robots before actual model training. Failed grasp attempts are collected, annotated, and prepared in advance, creating a ready-to-use training dataset that eliminates the need for each robot to individually collect and prepare training data
2Reliability
If more computational resources are used for model training, then the grasp model performance improves, but the on-board computing requirements for robots increase
Solution Approach 1:
The patent introduces a centralized server system as an intermediary between robots and the grasp model training process. The server handles all computationally intensive tasks including data preprocessing, model training, and validation, while robots only perform simple data collection and model execution. This distributes computational load away from individual robots, reducing their on-board computing requirements while maintaining high model performance through powerful centralized processing
3Device complexity
If robots use a basic grasp model, then the system complexity is reduced, but the robot fails to grasp objects securely or handle complex environments
Solution Approach 1:
The system implements a feedback mechanism where failed grasp attempts are collected from robots, annotated with failure reasons, and used to retrain and improve the grasp model. The model is continuously validated through simulation to ensure improvements are achieved. This iterative feedback loop enhances grasp success rate without requiring complex individual robot systems, as the centralized model learning handles the complexity
Data Source
AI summary
Methods, systems, and apparatus, including computer-readable media, for robot grasp learning. In some implementations, grasp data describing grasp attempts by robots is received. A set of the grasp attempts that represent unsuccessful grasp attempts is identified. Based on the set of grasp attempts representing unsuccessful grasp attempts, a grasp model based on sensor data for the unsuccessful grasp attempts. After training the grasp model, a performance level of the trained grasp model is verified based on one or more simulations of grasp attempts. In response to verifying the performance level of the trained grasp model, the trained grasp model is provided to one or more robots.