Ryo Fukano

HAPTIC DETECTION OF OBJECT AFFORDANCES BY A MULTI-FINGERED ROBOT HAND

This paper proposes a learning method for detecting object affordances through haptic exploration by a multi-fingered robot hand. Learning how to remove a screw cap from a bottle is the present target task. Assuming that coarse manipulation strategy is given by other methods, such as visual observation of a model task, the system applies coarse grabbing actions to the target object. In response to the exploratory actions, the object moves (rotates, in this case) along the physical constraint (screw). The robot detects the resulting motion through proprioception of the compliant fingers. A non-supervised statistical learning method is applied to categorize the resulting motion. The method is a combination of high-order local autocorrelation (HLAC), principal components analysis (PCA), and mean-shift clustering. Experiments with a real multi-fingered robot hand and bottle caps of different diameters confirm that the proposed method can detect and categorize rotational constraints.

Statistical manipulation learning of unknown objects by a multi-fingered robot hand

This paper proposes a learning method for multi-fingered manipulation of unknown objects. The method is a combination of higher-order local autocorrelation (HLAC), principal components analysis (PGA), and mean-shft clustering. Our results show that the different geometric restrictions of manipulation maximize the variance in the space of Feature vectors identified by HLAC analysis. As a result, the data corresponding to each manipulatory act are clustered in a high-dimensional space in accordance with the restrictions via PCA. Mean shift clustering method classify the clusters which correspond the restrictions. The efficacy of the proposed method is shown by means OF handling experiments of given diameter caps subjected to rotational restriction.