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The real-time estimation through vision of the physical properties of objects manipulated by humans is important to inform the control of robots for performing accurate and safe grasps of objects handed over by humans. However, estimating the 3D pose and dimensions of previously unseen objects using only RGB cameras is challenging due to illumination variations, reflective surfaces, transparencies, and occlusions caused both by the human and the robot. In this letter, we present a benchmark for dynamic human-to-robot handovers that do not rely on a motion capture system, markers, or prior knowledge of specific objects. To facilitate comparisons, the benchmark focuses on cups with different levels of transparencies and with an unknown amount of an unknown filling. The performance scores assess the overall system as well as its components in order to help isolate modules of the pipeline that need improvements. In addition to the task description and the performance scores, we also present and distribute as open source a baseline implementation for the overall pipeline to enable comparisons and facilitate progress.

The 3D localisation of an object and the estimation of its properties, such as shape and dimensions, are challenging under varying degrees of transparency and lighting conditions. In this paper, we propose a method for jointly localising container-like objects and estimating their dimensions using two wide-baseline, calibrated RGB cameras. Under the assumption of vertical circular symmetry, we estimate the dimensions of an object by sampling at different heights a set of sparse circumferences with iterative shape fitting and image re-projection to verify the sampling hypotheses in each camera using semantic segmentation masks. We evaluate the proposed method on a novel dataset of objects with different degrees of transparency and captured under different backgrounds and illumination conditions. Our method, which is based on RGB images only outperforms, in terms of localisation success and dimension estimation accuracy a deep-learning based approach that uses depth maps.

We investigate the problem of classifying -from a single imagethe level of content in a cup or a drinking glass. This problem is made challenging by several ambiguities caused by transparencies, shape variations and partial occlusions, and by the availability of only small training datasets. In this paper, we tackle this problem with an appropriate strategy for transfer learning. Specifically, we use adversarial training in a generic source dataset and then refine the training with a task-specific dataset. We also discuss and experimentally evaluate several training strategies and their combination on a range of container types of the CORSMAL Containers Manipulation dataset. We show that transfer learning with adversarial training in the source domain consistently improves the classification accuracy on the test set and limits the overfitting of the classifier to specific features of the training data.

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We present an audio-visual dataset recorded outdoors from a quadcopter and discuss baseline results for multiple applications. The dataset includes a scenario for source localization and sound enhancement with up to two static sources, and a scenario for source localization and tracking with a moving sound source. These sensing tasks are made challenging by the strong and time-varying ego-noise generated by the rotating motors and propellers. The dataset was collected using a small circular array with 8 microphones and a camera mounted on the quadcopter. The camera view was used to facilitate the annotation of the sound-source positions and can also be used for multi-modal sensing tasks. We discuss the audio-visual calibration procedure that is needed to generate the annotation for the dataset, which we make available to the research community 1 .