Robotic Object Detection with Rapid Fine-Tuning for Unknown Parcels
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Solution Overview
Problem
Existing robotic systems face challenges in efficiently and accurately detecting and grasping objects with unknown properties due to limited training datasets, leading to failures in parcel detection and inefficient operation in dynamic environments.
Innovation Solution
A method for rapidly updating object detection models using a small dataset and minimal human intervention, involving fine-tuning models with a single image and generating fine-tuned models to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data collection, annotation, and model training is performed to extend model ability, then detection accuracy for new parcel types is improved, but time consumption increases significantly (weeks)
Solution Approach 1:
The system performs preliminary actions by pre-training detection models on a diverse dataset of parcel appearances before deployment. When new parcel types are encountered, the system has already prepared models that can be quickly fine-tuned rather than trained from scratch, reducing the time penalty for improved detection accuracy.
Solution Approach 2:
The system changes training parameters by using a small subset of data (one or a few images) for fine-tuning instead of requiring large annotated datasets. This parameter change in data quantity and training duration allows the model to adapt to new parcel types rapidly while maintaining improved detection accuracy.
2Productivity
If a limited training dataset is used for machine learning models, then training time and computational resources are reduced, but the models fail to generalize beyond training appearances
Solution Approach 1:
The system segments the training process into two phases: initial pre-training on a diverse comprehensive dataset to build generalization capability, and subsequent fine-tuning on small task-specific datasets to maintain productivity. This segmentation allows the model to achieve both generalization and training efficiency.
Solution Approach 2:
The system performs preliminary training on a diverse dataset of parcel appearances before deployment, preparing models with broad generalization capability in advance. This preliminary action ensures that when new parcel types are encountered, the models already possess the foundational understanding needed to generalize effectively.
3Measurement precision
If the robot stops to perform manual data collection and model retraining, then detection accuracy for new parcels is improved, but operational continuity is disrupted
Solution Approach 1:
The system performs self-service by automatically detecting novel parcel types during operation, collecting minimal data (one or a few images), and fine-tuning models without human intervention. This self-service capability maintains both detection accuracy and operational continuity by eliminating the need for manual data collection and model retraining stops.
Solution Approach 2:
The system implements feedback by continuously monitoring detection performance during operation, identifying when new parcel types are encountered, and automatically initiating the fine-tuning process. This feedback loop ensures detection accuracy is maintained while minimizing disruption to operational continuity.
4Measurement precision
If multiple trained detection models are used to handle diverse parcel types, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The system applies dynamics by making the detection model set adaptable and changeable over time. Instead of maintaining a static large set of models, the system dynamically adds fine-tuned models only when new parcel types are detected, and manages model versions flexibly. This dynamic approach maintains detection accuracy while reducing model management complexity compared to static pre-defined model sets.
Data Source
AI summary
Method and apparatus for object detection by a robot are provided. The method comprises analyzing using a set of trained detection models, one or more first images of an environment of the robot to detect one or more objects in the environment of the robot, generating at least one fine-tuned model by training one or more of the trained detection models in the set, wherein the training is based on a second image of the environment of the robot and annotations associated with the second image, wherein the annotations identify one or more objects in the second image, updating the set of trained detection models to include the generated at least one fine-tuned model, and analyzing using the updated set of trained detection models, one or more third images of the environment of the robot to detect one or more objects in the environment.