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Dexterous robotic manipulation of unknown objects can open the way to novel tasks and applications of robots in semi-structured and unstructured settings, from advanced industrial manufacturing to exploration of harsh environments. However, it is challenging for at least three reasons: the desired motion of the object might be too complex to be described analytically, precise models of the manipulated objects are not available, the controller should simultaneously ensure both a robust grasp and an effective in-hand motion. To solve these issues we propose to learn in-hand robotic manipulation tasks from human demonstrations, using Dynamical Movement Primitives (DMPs), and to reproduce them with a robust compliant controller based on the Virtual Springs Framework (VSF), that employs real-time feedback of the contact forces measured on the robot fingertips. With this solution, the generalization capabilities of DMPs can be transferred successfully to the dexterous in-hand manipulation problem: we demonstrate this by presenting real-world experiments of in-hand translation and rotation of unknown objects.

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 problem challenging. 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 a fair and 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 audioonly 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 filling mass with audio-visual approaches, multi-stage algorithms reaches up to 65% weighted average capacity and mass scores. These results show that there is still room of improvement for the design of future methods that will be ranked and compared on the individual leaderboards provided by our open framework.

Grasp stability prediction of unknown objects is crucial to enable autonomous robotic manipulation in an unstructured environment. Even if prior information about the object is available, real-time local exploration might be necessary to mitigate object modelling inaccuracies. This paper presents an approach to predict safe grasps of unknown objects using depth vision and a dexterous robot hand equipped with tactile feedback. Our approach does not assume any prior knowledge about the objects. First, an object pose estimation is obtained from RGB-D sensing; then, the object is explored haptically to maximise a given grasp metric. We compare two probabilistic methods (i.e. standard and unscented Bayesian Optimisation) against random exploration (i.e. uniform grid search). Our experimental results demonstrate that these probabilistic methods can provide confident predictions after a limited number of exploratory observations, and that unscented Bayesian Optimisation can find safer grasps, taking into account the uncertainty in robot sensing and grasp execution.

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There are a wide variety of studies on player modeling. However, most of these studies target a specific game or genre. In some of these works, the number of in-game actions is used as a feature for modeling a player. However, using this feature leads to a complex model, and the model may miss some high-level relations among actions. In this paper, we propose a generic player modeling method that uses action-trait mapping relations which reveal correlations among actions. Mapping from the action-space to a much smaller trait-space improves interpretability of models. Additionally, to use the differences of impact of actions on player models, we apply feature weighting which uses the inverse of action frequencies. Players are clustered by Expectation Maximization. We demonstrate our method on a casual mobile game, Dusk Racer. We evaluate the feature weighting method using cluster validation with internal criteria. We conclude that using traits and feature weighting improves clustering quality and usability of the player model.

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