Active Data Selection for Robot Grasp Detection
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Solution Overview
Problem
Robot grasp method detection in unfamiliar environments faces high data labeling costs due to the large amount of labeled data required for deep learning models, which is inefficient and time-consuming, especially when dealing with unknown objects.
Innovation Solution
An active data selection method for robot grasp that combines a data feature extraction module, a grasp method detection module, and a data selection module, using a three-layer convolutional neural network for feature extraction and deconvolution to restore grasp parameters, and a data selection strategy that synchronizes training with the main network to select the most informative data for labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning methods are used for robot grasp method detection, then detection accuracy and generalization ability are improved, but the cost of data labeling increases significantly
Solution Approach 1:
The system automatically selects informative unlabeled data and generates pseudo-labels through the deep learning model itself, reducing dependency on manual annotation. The active learning loop enables the model to self-improve by identifying and labeling only the most valuable data points.
Solution Approach 2:
An active learning framework is introduced as an intermediary between labeled and unlabeled data. This framework includes a selection module that identifies informative samples and a pseudo-labeling mechanism that generates training labels, thereby bridging the gap between limited labeled data and abundant unlabeled data.
2Reliability
If a large amount of labeled data is collected for training in unfamiliar environments, then the detection performance is improved, but the time and labor cost increases
Solution Approach 1:
Instead of labeling all available data, the system labels only a partial subset of the most informative data points identified by the active learning selection module. This partial action approach achieves satisfactory detection performance with significantly reduced labeling effort and time.
Solution Approach 2:
The system performs preliminary evaluation of unlabeled data to identify informative samples before committing resources to labeling. The selection module pre-screens data based on uncertainty metrics, ensuring that only high-value samples are labeled, thereby optimizing the time investment.
3Productivity
If traditional deep learning training is used, then the model can learn from available data, but redundant data cannot be identified and labeling efficiency is low
Solution Approach 1:
The active learning selection module extracts and isolates the most informative data points from the unlabeled pool based on uncertainty measurements. By taking out only these high-value samples for labeling, the system eliminates redundancy and focuses computational resources on learning from the most critical data.
Solution Approach 2:
The system changes the parameter of data selection from random or uniform sampling to uncertainty-based sampling. By adjusting the selection criterion to prioritize samples with higher prediction uncertainty, the system transforms the training process to focus on informative regions of the data distribution.
Data Source
AI summary
The present invention belongs to the technical field of computer vision and provides a data active selection method for robot grasping. The core content of the present invention is a data selection strategy module, which shares the feature extraction layer of backbone main network and integrates the features of three receptive fields with different sizes. While making full use of the feature extraction module, the present invention greatly reduces the amount of parameters that need to be added. During the training process of the main grasp method detection network model, the data selection strategy module can be synchronously trained to form an end-to-end model. The present invention makes use of naturally existing labeled and unlabeled labels, and makes full use of the labeled data and the unlabeled data. When the amount of the labeled data is small, the network can still be more fully trained.