Experiments on Learning Based Industrial Bin-picking with Iterative Visual Recognition
This work addresses robotic automation challenges in industrial settings, but it is incremental as it builds on existing learning and vision techniques.
The paper tackles the problem of robotic bin-picking by combining learning-based methods with iterative visual recognition to improve success rates, showing that merging multiple depth images reduces failures due to occlusion and that training requires only a small dataset.
This paper shows experimental results on learning based randomized bin-picking combined with iterative visual recognition. We use the random forest to predict whether or not a robot will successfully pick an object for given depth images of the pile taking the collision between a finger and a neighboring object into account. For the discriminator to be accurate, we consider estimating objects' poses by merging multiple depth images of the pile captured from different points of view by using a depth sensor attached at the wrist. We show that, even if a robot is predicted to fail in picking an object with a single depth image due to its large occluded area, it is finally predicted as success after merging multiple depth images. In addition, we show that the random forest can be trained with the small number of training data.