5 accepted papers
Robotic sorting in cluttered environments still faces significant challenges, especially with resource-constrained hardware. Traditional detect-and-grasp workflows usually require extensive image collection and annotation for model training, which can become impractical when the categories of the so
Datasets often include noisy labels, but learning from them is difficult. Since mislabeled examples usually have larger loss values in training, the small-loss trick is regarded as a standard metric to identify the clean example from the training set for better performance. Nonetheless, this proposa…
Label errors have been found to be prevalent in popular text, vision, and audio datasets, which heavily influence the safe development and evaluation of machine learning algorithms. Despite increasing efforts towards improving the quality of generic data types, such as images and texts, the problem…
Many soft robots are made of hyperelastic silicone rubbers and usually experience large nonlinear deformations in actions. Accurate material parameters are essential to provide reliable analysis and support robot designs. This letter presents a practical material parameter determination method by it
It is challenging to remove rain steaks from a single rainy image because the rain steaks are spatially varying in the rainy image. On top of a new insight in single image de-raining, a nonlocal de-raining algorithm is proposed in this paper to remove the rain streaks from the rainy image. The rainy…