What are the steps to train an object detection and recognition model for robotics using computer vision?

This seems to be the hardest part of the whole process as in order to be successful you need huge amount of well described data. Fortunately as other AI models evolved we can use them to prepare or sort existng data and make this process easier.

Robotics object identification and recognition require a trained model. This involves collecting a large dataset of object-containing images, manually annotating them, choosing an object detection and recognition model architecture, training the model with the dataset, evaluating its performance on a validation dataset, and deploying it on the robotic platform. Maintaining the model's effectiveness requires constant monitoring and refinement. Images from real-world locations, simulations, and internet databases are collected, taking into account lighting, backdrop, object orientations, and occlusion levels. Manually labelling objects with classes is data annotation.

Seamless object detection and recognition in robotics rely on model deployment optimization techniques like quantization and pruning. Quantization reduces model size and improves inference speed by converting floating-point weights to integers. Pruning removes redundant connections, further enhancing efficiency without compromising accuracy. On a platform like the NVIDIA Jetson hardware, NVIDIA TensorRT and NVIDIA DeepSparse facilitate quantization and pruning, respectively, empowering robots to excel in resource-constrained environments. Quantization and pruning offer reduced latency, reduced memory footprint, enhanced scalability, and reduced deployment costs, making them essential for real-world robotics applications.

Object detection and recognition model training for robotics involves several steps, including data collection and preprocessing, choosing a model architecture, training the model, evaluating the model, and deploying the model. Data collection involves collecting a large and diverse dataset of images containing objects to be recognized. The model architecture, such as YOLO, SSD, or Faster R-CNN, is chosen based on specific requirements. After training the model, it is evaluated on a held-out dataset using metrics like precision, recall, and mean average precision (mAP). Once satisfied with the model's performance, it is deployed to the robotic system, integrating it with the robot's software and hardware.