Optometrist's Algorithm for Personalizing Robot-Human Handovers

With an increasing interest in human-robot collaboration, there is a need to develop robot behavior while keeping the human user's preferences in mind. Highly skilled human users doing delicate tasks require their robot partners to behave according to their work habits and task constraints. To achieve this, we present the use of the Optometrist's Algorithm (OA) to interactively and intuitively personalize robot-human handovers. Using this algorithm, we tune controller parameters for speed, location, and effort. We study the differences in the fluency of the handovers before and after tuning and the subjective perception of this process in a study of $N=30$ non-expert users of mixed background -- evaluating the OA. The users evaluate the interaction on trust, safety, and workload scales, amongst other measures. They assess our tuning process to be engaging and easy to use. Personalization leads to an increase in the fluency of the interaction. Our participants utilize the wide range of parameters ending up with their unique personalized handover.Comment: 7 pages, 5 figures. Accepted at IEEE-ROMAN 2023. For more information visit: https://github.com/vivekgupte07/optometrist-algorithm-handover

Object-Independent Human-to-Robot Handovers using Real Time Robotic Vision

We present an approach for safe and object-independent human-to-robot handovers using real time robotic vision and manipulation. We aim for general applicability with a generic object detector, a fast grasp selection algorithm and by using a single gripper-mounted RGB-D camera, hence not relying on external sensors. The robot is controlled via visual servoing towards the object of interest. Putting a high emphasis on safety, we use two perception modules: human body part segmentation and hand/finger segmentation. Pixels that are deemed to belong to the human are filtered out from candidate grasp poses, hence ensuring that the robot safely picks the object without colliding with the human partner. The grasp selection and perception modules run concurrently in real-time, which allows monitoring of the progress. In experiments with 13 objects, the robot was able to successfully take the object from the human in 81.9% of the trials.Comment: IEEE Robotics and Automation Letters (RA-L). Preprint Version. Accepted September, 2020. The code and videos can be found at https://patrosat.github.io/h2r_handovers

Improving Generalization of Deep Networks for Estimating Physical Properties of Containers and Fillings

We present methods to estimate the physical properties of household containers and their fillings manipulated by humans. We use a lightweight, pre-trained convolutional neural network with coordinate attention as a backbone model of the pipelines to accurately locate the object of interest and estimate the physical properties in the CORSMAL Containers Manipulation (CCM) dataset. We address the filling type classification with audio data and then combine this information from audio with video modalities to address the filling level classification. For the container capacity, dimension, and mass estimation, we present a data augmentation and consistency measurement to alleviate the over-fitting issue in the CCM dataset caused by the limited number of containers. We augment the training data using an object-of-interest-based re-scaling that increases the variety of physical values of the containers. We then perform the consistency measurement to choose a model with low prediction variance in the same containers under different scenes, which ensures the generalization ability of the model. Our method improves the generalization ability of the models to estimate the property of the containers that were not previously seen in the training