No abstract

In this paper a bio-inspired control architecture for a robotic hand is presented. It relies on the same mechanisms of learning inverse internal models studied in humans. The control is capable of developing an internal representation of the hand interacting with the environment and updating it by means of the interaction forces that arise during contact. The learning paradigm exploits LWPR networks, which allow efficient incremental online learning through the use of spatially localized linear regression models. Additionally this paradigm limits negative interference when learning multiple tasks. The architecture is validated on a simulated finger of the DLR-HIT-Hand II performing closing movements in presence of two different viscous force fields, perturbing its motion.

Underactuated compliant swimming robots are characterized by a simple mechanical structure, capable to mimic the body undulation of many fish species. One of the design issue for these robots is the generation and control of best performing swimming gaits. In this paper we propose a new controller, based on AFO oscillators, to address this issue. After analyzing the effects of the motion on the robot natural frequencies, we show that the closed loop system is able to generate self-sustained oscillations, at a characteristic frequency, while maximizing swimming velocity.

The authors studied a case of mucin-producing adenoma of the thyroid gland. The tumor consisted almost entirely of signet-ring cells containing mucin, which was strongly positive with PAS, with and without diastase pre-treatment, and Alcian blue stain at pH 2.5. Immunoperoxidase staining for thyreoglobulin was clearly positive within the cytoplasm of signet-ring cells and also in the follicle material, which indicates that the tumor derived from follicular epithelium.