Robotic object detection: learning to improve the classifiers using sparse graphs for path planning

Object detection is a basic skill for a robot to perfor-m tasks in human environments. In order to build a good object classifier, a large training set of la-beled images is required; this is typically collect-ed and labeled (often painstakingly) by a human. This method is not scalable and therefore limits the robot’s detection performance. We propose an algorithm for a robot to collect more data in the environment during its training phase so that in the future it could detect objects more reli-ably. The first step is to plan a path for collecting additional training images, which is hard because a previously visited location affects the decision for the future locations. One key component of our work is path planning by building a sparse graph that captures these dependencies. The other key component is our learning algorithm that weighs the errors made in robot’s data collection process while updating the classifier. In our experiments, we show that our algorithms enable the robot to im-prove its object classifiers significantly.