Santiago Donaher

Alessio Xompero, Santiago Donaher, Vladimir Iashin et al.

The contactless estimation of the weight of a container and the amount of its content manipulated by a person are key pre-requisites for safe human-to-robot handovers. However, opaqueness and transparencies of the container and the content, and variability of materials, shapes, and sizes, make this estimation difficult. In this paper, we present a range of methods and an open framework to benchmark acoustic and visual perception for the estimation of the capacity of a container, and the type, mass, and amount of its content. The framework includes a dataset, specific tasks and performance measures. We conduct an in-depth comparative analysis of methods that used this framework and audio-only or vision-only baselines designed from related works. Based on this analysis, we can conclude that audio-only and audio-visual classifiers are suitable for the estimation of the type and amount of the content using different types of convolutional neural networks, combined with either recurrent neural networks or a majority voting strategy, whereas computer vision methods are suitable to determine the capacity of the container using regression and geometric approaches. Classifying the content type and level using only audio achieves a weighted average F1-score up to 81% and 97%, respectively. Estimating the container capacity with vision-only approaches and estimating the filling mass with audio-visual multi-stage approaches reach up to 65% weighted average capacity and mass scores. These results show that there is still room for improvement on the design of new methods. These new methods can be ranked and compared on the individual leaderboards provided by our open framework.

Santiago Donaher, Alessio Xompero, Andrea Cavallaro

Food containers, drinking glasses and cups handled by a person generate sounds that vary with the type and amount of their content. In this paper, we propose a new model for sound-based classification of the type and amount of content in a container. The proposed model is based on the decomposition of the problem into two steps, namely action recognition and content classification. We use the scenario of the recent CORSMAL Containers Manipulation dataset and consider two actions (shaking and pouring), and seven combinations of material and filling level. The first step identifies the action performed by a person with the container. The second step determines the amount and type of content using an action-specific classifier. Experiments show that the proposed model achieves 76.02, 78.24, and 41.89 weighted average F1 score on the three test sets, respectively, and outperforms baselines and existing approaches that classify the content amount and type either independently or jointly.