Experimental Validation of Contact Dynamics for In-Hand Manipulation

This paper evaluates state-of-the-art contact models at predicting the motions and forces involved in simple in-hand robotic manipulations. In particular it focuses on three primitive actions --linear sliding, pivoting, and rolling-- that involve contacts between a gripper, a rigid object, and their environment. The evaluation is done through thousands of controlled experiments designed to capture the motion of object and gripper, and all contact forces and torques at 250Hz. We demonstrate that a contact modeling approach based on Coulomb's friction law and maximum energy principle is effective at reasoning about interaction to first order, but limited for making accurate predictions. We attribute the major limitations to 1) the non-uniqueness of force resolution inherent to grasps with multiple hard contacts of complex geometries, 2) unmodeled dynamics due to contact compliance, and 3) unmodeled geometries dueto manufacturing defects.Comment: International Symposium on Experimental Robotics, ISER 2016, Tokyo, Japa

Experimental evaluation of tree-based algorithms for intonational breaks representation

The prosodic specification of an utterance to be spoken by a Textto-Speech synthesis system can be devised in break indices, pitch accents and boundary tones. In particular, the identification of break indices formulates the intonational phrase breaks that affect all the forthcoming prosody-related procedures. In the present paper we use tree-structured predictors, and specifically the commonly used in similar tasks CART and the introduced C4.5 one, to cope with the task of break placement in the presence of shallow textual features. We have utilized two 500-utterance prosodic corpora offered by two Greek universities in order to compare the machine learning approaches and to argue on the robustness they offer for Greek break modeling. The evaluation of the resulted models revealed that both approaches were positively compared with similar works published for other languages, while the C4.5 method accuracy scaled from 1% to 2,7% better than CART. © Springer-Verlag Berlin Heidelberg 2005

Evaluation of Corpus Based Tone Prediction in Mismatched Environments for Greek TtS Synthesis

One of the main aspects in Text-to-Speech (TtS) synthesis is the successful prediction of tonal events. In this work we deal with the evaluation of corpus-based models in operational environments other than the training ones. Two pitch accent frameworks derived by linguistically enriched speech data from a generic domain and a limited domain were initially evaluated by applying the 10-fold cross validation method. As a second step, we utilized the cross domains data validation. Due to the heterogeneity of the data, we further employed three machine learning approaches, CART, Naive Bayes and Bayesian networks. The results demonstrate that the limited domain models achieve in average 10 % improved accuracy in self-domain evaluation, while the generic models preserve a their performance regardless the domain of application. 1