Design Of Behaviors For Robots Research Articles

AutoMoDe-Mate: Automatic off-line design of spatially-organizing behaviors for robot swarms

• Automatic methods can design spatially-organizing behaviors for robot swarms. • AutoMoDe-Mate is a design method specialized in spatial organization problems. • AutoMoDe-Mate includes a module that enables robot pattern formation. • Experiments with physical robots show effectiveness in coverage and networking. • AutoMoDe-Mate outperforms baseline modular and neuro-evolutionary methods. We present Mate , an automatic off-line design method specialized in the design of spatially-organizing behaviors for robot swarms. Mate belongs to the family of modular methods known as AutoMoDe. We introduce Mate to study the automatic design of collective behaviors for missions in which the swarm is subject to spatial distribution constrains. In this paper, we produce control software for three missions with specifications related to the distribution of the swarm in the environment. We conduct experiments in simulation and with a swarm of 20 e-puck robots. Alongside Mate , we also conduct experiments with two other automatic design methods: Chocolate —a state-of-the-art instance of AutoMoDe; and EvoSpace —a method based on neuro-evolution. Early studies conducted with existing modular design methods have shown their limitations in the design of spatially-organizing behaviors for robots that operate under spatial constrains. By introducing a specialized method like Mate , we expect to overcome these limitations. The aggregate results of our experiments show that Mate performs significantly better than Chocolate and EvoSpace in the missions we consider.

From When to When: Evaluating Naturalness of Reaction Time via Viewing Turn around Behaviors

This paper addresses the effects of visual reaction times of a turn around behavior toward touch stimulus in the context of perceived naturalness. People essentially prefer a quick and natural reaction time to interaction partners, but appropriate reaction times will change due to the kinds of partners, e.g., humans, computers, and robots. In this study, we investigate two visual reaction times in touch interaction: the time length from the touched timing to the start of a reaction behavior, and the time length of the reaction behavior. We also investigated appropriate reaction times for different beings: three robots (Sota, Nao and Pepper) and humans (male and female). We conducted a web-survey based experiment to investigate natural reaction times for robots and humans, and the results concluded that the best combinations of both reaction times are different between each robot (i.e., among Sota, Nao and Pepper) and the humans (i.e., between male and female). We also compared the effect of using the best combinations for each robot and human to prove the importance of using each appropriate reaction timing for each being. The results suggest that an appropriate reaction time combination investigated from the male model is not ideal for robots, and the combination investigated from the female model is a better choice for robots. Our study also suggests that calibrating parameters for individual robots’ behavior design would enable better performances than using parameters of robot behaviors based on observing human-human interaction, although such an approach is a typical method of robot behavior design.

Automatic Design of Collective Behaviors for Robots that Can Display and Perceive Colors

Research in swarm robotics has shown that automatic design is an effective approach to realize robot swarms. In automatic design methods, the collective behavior of a swarm is obtained by automatically configuring and fine-tuning the control software of individual robots. In this paper, we present TuttiFrutti: an automatic design method for robot swarms that belongs to AutoMoDe—a family of methods that produce control software by assembling preexisting software modules via optimization. The peculiarity of TuttiFrutti is that it designs control software for e-puck robots that can display and perceive colors using their RGB LEDs and omnidirectional camera. Studies with AutoMoDe have been so far restricted by the limited capabilities of the e-pucks. By enabling the use of colors, we significantly enlarge the variety of collective behaviors they can produce. We assess TuttiFrutti with swarms of e-pucks that perform missions in which they should react to colored light. Results show that TuttiFrutti designs collective behaviors in which the robots identify the colored light displayed in the environment and act accordingly. The control software designed by TuttiFrutti endowed the swarms of e-pucks with the ability to use color-based information for handling events, communicating, and navigating.

Examples of Gibsonian Affordances in Legged Robotics Research Using an Empirical, Generative Framework.

Evidence from empirical literature suggests that explainable complex behaviors can be built from structured compositions of explainable component behaviors with known properties. Such component behaviors can be built to directly perceive and exploit affordances. Using six examples of recent research in legged robot locomotion, we suggest that robots can be programmed to effectively exploit affordances without developing explicit internal models of them. We use a generative framework to discuss the examples, because it helps us to separate—and thus clarify the relationship between—description of affordance exploitation from description of the internal representations used by the robot in that exploitation. Under this framework, details of the architecture and environment are related to the emergent behavior of the system via a generative explanation. For example, the specific method of information processing a robot uses might be related to the affordance the robot is designed to exploit via a formal analysis of its control policy. By considering the mutuality of the agent-environment system during robot behavior design, roboticists can thus develop robust architectures which implicitly exploit affordances. The manner of this exploitation is made explicit by a well constructed generative explanation.

Beyond black-box optimization: a review of selective pressures for evolutionary robotics

Evolutionary robotics is often viewed as the application of a family of black-box optimization algorithms -- evolutionary algorithms - - to the design of robots, or parts of robots. When considering evolutionary robotics as black-box optimization, the selective pressure is mainly driven by a user-defined, black-box fitness function, and a domain-independent selection procedure. However, most evolutionary robotics experiments face similar challenges in similar setups: the selective pressure, and, in particular, the fitness function, is not a pure user-defined black box. The present review shows that, because evolutionary robotics experiments share common features, selective pressures for evolutionary robotics are a subject of research on their own. The literature has been split into two categories: goal refiners, aimed at changing the definition of a good solution, and process helpers, designed to help the search process. Two sub-categories are further considered: task-specific approaches, which require knowledge on how to solve the task and task-agnostic ones, which do not need it. Besides highlighting the diversity of the approaches and their respective goals, the present review shows that many task-agnostic process helpers have been proposed during the last years, thus bringing us closer to the goal of a fully automated robot behavior design process.

BEHAVE-II: The Revised Set of Measures to Assess Users’ Attitudinal and Behavioral Responses to a Social Robot

In order to inform the design of behaviors for robots that share domestic and public spaces with people, it is important to know what robot behavior is considered as normative. The work reported in this paper stems from the premise that what is perceived as socially normative behavior for people may differ from what is considered socially normative for a robot. This paper details the development of a data collection instrument, BEHAVE-II, for assessing user responses toward a robot’s behavior using both attitudinal and behavioral responses. To test the validity and reliability of the BEHAVE-II instrument, a human-robot interaction experiment was conducted in which a robot or a human invaded the personal space of a participant. We found that participants’ reactions were stronger when their personal space was invaded by a robot compared with a person. This points to the fact that humans are actually highly sensible whether robots’ adhere to social norms which underlines the importance of the BEHAVE-II instrument.

Truly-Tender-Tailed Tag-Playing Robot Interface Through Friendly Amusing Mobile Function

To expand use of the mobile robot Step-On Interface (SOI), originally targeting maintenance, training, and recovery of human physical and cognitive functions, we introduce a “Truly-Tender-Tailed” (T3, pronounced tee-cube) tag-playing robot as a “Friendly Amusing Mobile” (FAM) function. Displaying a previously prepared bitmap (BMP) image and speeding up display make it easy to design button placement and other screen parameters using a painting software package. The BMP-image scope matrix simplifies step detection and recognition and the motion trajectory design editor facilitates robot behavior design.

630 「意識」に着目したロボット行動の設計

630 「意識」に着目したロボット行動の設計

Soft computing techniques for the design of mobile robot behaviors

This paper describes a soft computing approach to robot behavior design. Evolutionary algorithms adapt a wall following behavior implemented by means of fuzzy control rules. A messy coding scheme for fuzzy rules reduces the size and complexity of the rule base, with the result, that the fuzzy control design remains tractable for the genetic algorithm. An evolution strategy tunes the scaling factors for the controller’s input and output and optimizes the adjustment of the sensors on the robot. A neural network classifies the basic geometric features of the local environment based on previous perceptions of the sonar sensors.