Two Methodologies Toward Artificial Tactile Affordance System in Robotics

Abstract

Abstract-If the theory of affordance is applied to a robot, performing the whole process of recognition and planning is not always required in its computer. Since the tactile sensing of a robot is important to perform any task, we focus on tactile sensing and introduce a new concept called the artificial tactile affordance system (ATAS). Its basic idea is the implementation of a recurrent mechanism in which information obtained from the object and the behavior performed by the robot's inducing the next behavior. We intend to implement ATAS based on the following two methodologies: (1) after each rule is transformed into an algorithm, a program module is coded based on the algorithm; ATAS is composed of several program modules, and a module is selected from the set of modules based on sensor information; (2) a set of rules is expressed as a table composed of sensor input columns and behavior output columns, and the table rows correspond to rules; since each rule is transformed to a string of 0 and 1, we treat a long string composed of rule strings as a gene to obtain an optimum gene that adapts to its environment using a genetic algorithm (GA). For methodology 1, we established an ATAS composed of 3 to 5 modules to accomplish such tasks as object grasping, pick and place, cap screwing, and assembling. Using methodology 1, a two-hand-arm robot equipped with an optical threeaxis tactile sensor performed the above tasks. For methodology 2, we propose the Evolutionary Behavior Table System (EBTS) that uses a GA to acquire the autonomous cooperation behavior of multiple mobile robots. In validation experiments, three agents equipped with behavior tables conveyed an object to a specified goal with higher scores than the four-agent condition. Since the redundant agent does not interrupt the other agents, the agent acquires the collective behavior of not interrupting other agents based on its environment information. Methodology 1 is very effective for such fine control as handling tasks of humanoid robots, and methodology 2 is very useful to obtain general robotic behavior that is suitable for the environment.

Bottom-Up Approach for Behavior Acquisition of Agents Equipped With Multi-Sensors

“…EBTS explained in the subsequent chapter is produced based on this bottom-up architecture as one of the achieved artificial affordance systems [6].…”

“…System (EBTS) [6] [7] proposed by us as one of the effective design methods for intellectual behavior. In this method, after patterns of actuator output for specific sensor inputs are defined as gene information, the actuator output is optimized using a genetic algorithm (GA) to make the robot generate appropriate behavior adapted to its environment.…”

“…In our first report, in which EBTS is introduced to achieve artificial tactile affordance (ATAS), we concluded that EBTS is effective for the object transfer problem of multiple robots, but it is not always adequate for manipulation of multi-finger hands [6]. However, in the subsequent report, computer experimental results showed that EBTS was applied to the manipulation problem in a specific workspace [7].…”

Object Handling Precision Using Mouse-like Haptic Display Generating Tactile and Force Sensation

“…For object manipulation in virtual space, the display device has become a key part for stimulating the human senses accurately [6][7]. A tactile and haptic presentation is effective because it involves the tactile sensation and reaction force caused by touching an object [8][9].…”

Edge Extraction using Image and Three-Axis Tactile Data

“…Our research group studies the Artificial Tactile Affordance System (ATAS) to control a robot based on interaction between a robotic hand and the environment trough the above trial. In a previous study [1], our robotic hand was able to obtain the ideal trajectory of fingers to twist a bottle cap based on tactile data acquired through handling the cap. Although the tactile sensor can obtain geometrical data as real scale, it cannot survey a wide area; its scanning is restricted to around the contact area between the finger and sensor.…”