Active Control for Object Perception and Exploration with a Robotic Hand

Abstract

Abstract. We present an investigation on active control for intelligent object exploration using touch with a robotic hand. First, uncertainty from the exploration is reduced by a probabilistic method based on the accumulation of evidence through the interaction with an object of interest. Second, an intrinsic motivation approach allows the robot hand to perform intelligent active control of movements to explore interesting locations of the object. Passive and active perception and exploration were implemented in simulated and real environments to compare their benefits in accuracy and reaction time. The experiments for validation of the proposed method were performed with an object recognition task, using a robotic platform composed by a three-fingered robotic hand and a robot table. The results demonstrate that our method permits to the robotic hand to achieve high accuracy for object recognition with low impact on the reaction time required to perform the task. These benefits make the proposed method suitable for perception and exploration in autonomous robotics.

Bayesian perception of touch for control of robot emotion

“…Examples of facial expressions activated from the perceived touch during humanrobot interaction are shown in Figure 7. All the modules in the control architecture were developed in C/C++ language, whilst communication and synchronisation of modules were handled with the YARP (Yet Another Robot Platform) library [28], which has demonstrated to provide robust control in multiple robotic platforms and applications [29,30,31,32].…”