Children diagnosed with anxiety disorders are taught a range of strategies to navigate situations of heightened anxiety. Techniques such as deep breathing and repetition of mantras are commonly employed, as they are known to be calming and reduce elevated heart rates. Although these strategies are often effective, their successful application relies on prior training of the children for successful use when faced with challenging situations. This paper investigates a pocket-sized companion robot designed to offer a relaxation technique requiring no prior training, with a focus on immediate impact on the user’s heart rate. The robot utilizes a tactile game to divert the user’s attention, thereby promoting relaxation. We conducted two studies with children who were not diagnosed with anxiety: a 14-day pilot study with two children (age 8) and a main study with 18 children (ages 7-8). Both studies employed a within-subjects design and focused on measuring heart rate during tactile interaction with the robot and during non-use. Interacting with the robot was found to significantly lower the study participants’ heart rate (p<0.01) compared to the nonuse condition, indicating a consistent calming effect across all participants. These results suggest that tactile companion robots have the potential to enhance the therapeutic value of relaxation techniques.

Materials with the ability to self-classify their own shape have the potential to advance a wide range of engineering applications and industries. Biological systems possess the ability not only to self-reconfigure but also to self-classify themselves to determine a general shape and function. Previous work into modular robotics systems has only enabled self-recognition and self-reconfiguration into a specific target shape, missing the inherent robustness present in nature to self-classify. In this paper we therefore take advantage of recent advances in deep learning and neural cellular automata, and present a simple modular 2D robotic system that can infer its own class of shape through the local communication of its components. Furthermore, we show that our system can be successfully transferred to hardware which thus opens op-portunities for future self-classifying machines. Code available at https://github.com/kattwalker/projectcube. Video available at https://youtu.be/0TCOkE4keyc.

Speech characteristics have shown potential as a tool to identify personality factors in humans but usually demands longer interactions or elaborate sensor requirements. This paper presents a novel robot system that uses speech and body movement characteristics to recognize and distinguish between users-groups interacting with it through small sets of interactions. It clusters people with similar characteristics together and measures the affective impact of specific robot behaviors. The system was tested using a custom-created affective robot through 36 interactions with 6 human participants aging from 11 to 70. 186 samples were collected in two different physical contexts and the similarity of the samples for each user was compared. The preliminary results indicate that the speech and movement characteristics have the potential as a tool to recognize specific users and as a guide to form user groups. This was found using only basic sensors available in most robots through a limited set of interactions. The results further highlight that there are significant differences between measurements for the same users in different physical contexts meaning that the participants move and talk differently with each context. This paper suggests combining the speech and movement characteristics with information on the physical context to gain better user adaptation in robot behaviors for future projects.

This paper investigates the specific scenario of high-intensity confrontations between humans and robots, to understand how robots can defuse the conflict. It focuses on the effectiveness of using five different affective expression modalities as main drivers for defusing the conflict. The aim is to discover any strengths or weaknesses in using each modality to mitigate the hostility that people feel towards a poorly performing robot. The defusing of the situation is accomplished by making the robot better at acknowledging the conflict and by letting it express remorse. To facilitate the tests, we used a custom affective robot in a simulated conflict situation with 105 test participants. The results show that all tested expression modalities can successfully be used to defuse the situation and convey an acknowledgment of the confrontation. The ratings were remarkably similar, but the movement modality was different (ANON p<; .05) than the other modalities. The test participants also had similar affective interpretations on how impacted the robot was of the confrontation across all expression modalities. This indicates that defusing a high-intensity interaction may not demand special attention to the expression abilities of the robot, but rather require attention to the abilities of being socially aware of the situation and reacting in accordance with it.

In an effort to improve how robots function in social contexts, this paper investigates if a robot that actively shares a reaction to an event with a human alters how the human perceives the robot's affective impact. To verify this, we created two different test setups. One to highlight and isolate the reaction element of affective robot expressions, and one to investigate the effects of applying specific timing delays to a robot reacting to a physical encounter with a human. The first test was conducted with two different groups (n=84) of human observers, a test group and a control group both interacting with the robot. The second test was performed with 110 participants using increasingly longer reaction delays for the robot with every ten participants. The results show a statistically significant change (p<; .05) in perceived affective impact for the robots when they react to an event shared with a human observer rather than reacting at random. The result also shows for shared physical interaction, the near-human reaction times from the robot are most appropriate for the scenario. The paper concludes that a delay time around 200ms may render the biggest impact on human observers for small-sized non-humanoid robots. It further concludes that a slightly shorter reaction time around 100ms is most effective when the goal is to make the human observers feel they made the biggest impact on the robot.

Many factors influence the evolvability of populations, and this article illustrates how intrinsic mortality (death induced through internal factors) in an evolving population contributes favorably to evolvability on a fixed deceptive fitness landscape. We test for evolvability using the hierarchical if-and-only-if (h-iff) function as a deceptive fitness landscape together with a steady state genetic algorithm (SSGA) with a variable mutation rate and indiscriminate intrinsic mortality rate. The mutation rate and the intrinsic mortality rate display a relationship for finding the global maximum. This relationship was also found when implementing the same deceptive fitness landscape in a spatial model consisting of an evolving population. We also compared the performance of the optimal mutation and mortality rate with a state-of-the-art evolutionary algorithm called age-fitness Pareto optimization (AFPO) and show how the two approaches traverse the h-iff landscape differently. Our results indicate that the intrinsic mortality rate and mutation rate induce random genetic drift that allows a population to efficiently traverse a deceptive fitness landscape. This article gives an overview of how intrinsic mortality influences the evolvability of a population. It thereby supports the premise that programmed death of individuals could have a beneficial effect on the evolvability of the entire population.

This paper investigates the potential benefits of augmenting audio-based affective means of expression to strengthen the perceived intentions of a robot. Robots are often viewed as being simple machines with limied capabilities of communication. Changing how a robot is perceived, towards a more affective interpretation of its intentions, requires careful consideration of the means of expression available to the robot. It also requires alignment between these means to ensure they work in coordination with each other to make the robot easier to understand. As an effort to strengthen the affective interpretation of a soft robotic arm robot, we altered its overall expression by changing the available audio-based expression modalities. The system mitigatedthe naturally occurring noise from actuators and pneumatic systems and used a custom sound that supported the movement of the robot. The robot was tested by interacting with human observers (n=78) and was perceived as being significantly more curious, happy and less angry when augmented by audio that aligned with the naturally occurred robot sounds. The results show that the audio-based expression modality of robots is a valuable communication tool to consider augmenting when designing robots that convey affective information.

This paper provides a survey of the different means of expression employed by robots conveying affective states to human recipients. The paper introduces a model of affective expression modalities (MOAM) that describes and compares the emphasis on specific means of expression and applies it to the surveyed robots. Using the model entails viewing the effect of applied expression modalities in light of how well the robot responds to external stimuli and with attention to how aligned the robot's means of affective expressions are with the intended working scenario. The model-based survey shows that a majority (85%) of the surveyed robots contain a category with room for additional affective means of expression, and a quarter (25.6%) of the robots use a single or two affective expression modalities to convey affective states. The result of the survey indicates there is an under-researched opportunity in exploring synergies between the different expression modalities to amplify the overall affective impact of a robot.

Liquid handling robots are rarely used in the domain ofartificial life. In this field, transitory behaviours of nonequilibrium man-made systems are studied and need an automatic monitoring and logging of results. In addition, artificial life experiments are dynamic with frequent changes, which makes it difficult to apply conventional liquid handling robots as they are designed to automate a pre-defined task.In order to address these issues, we have developed an open source liquid handling robot, EvoBot. It uses a modular approach, which gives us the possibility to reconfigure the robot for different experiments and make it possible for users to add functionality by just developing a function specific module. In addition, it provides sensors and extra functionality for monitoring an experiment, which allows researchers to perform interactive experiments with the aim of prolongingnon-equilibrium conditions. In this paper, we describe the modular design of EvoBot, document its performance, and provide a novel example of an interactive experiment in artificial life, where the robot nurtures a microbial fuel cell based on its voltage output.

Due to the replacement of natural flora and fauna with ur- ban environments, a significant part of the earth’s organisms that function as primary consumers have been dispelled. To compensate for the reduction in the amount of primary con- sumers, robotic systems that mimic plant-like organisms are interesting to mimic for their potential functional and aes- thetic value in urban environments. To investigate how to utilize plant developmental strategies in order to engender ur- ban artificial plants, we built a simple evolutionary model that applies an L-System based grammar as an abstraction of plant development. In the presented experiments, phytomorpholo- gies (plant morphologies) are iteratively constructed using a context sensitive L-System. The genomic representation of the L-System is subject to mutation by an evolutionary al- gorithm. These mutations thus alter the developmental rules of these phytomorphologies. We compare the differences be- tween the light absorption of evolving virtual plants that re- main static during their life and virtual plants that possess the possibility to move joints that link the separate parts of the virtual plants. Our results show that our evolutionary al- gorithm did not exploit potential beneficial joint actuation, instead, mostly static structures evolved. The results of our evolving L-System show that it is able to create various phy- tomorphologies, albeit that the results are preliminary and will be more thoroughly investigated in the future

We describe experimentation with robots together with computer vision to improve various artificial chemical life systems. We investigate a case study, where the use of the robot enables timely, and precise verification of the experiment results with fixed parameters. In addition, new possibilities to promote the quality of artificial life experiments, such as positioning droplets at equidistant intervals or in patterns are discussed.

We present a robotic platform based on the open source RepRap 3D printer that can print and maintain chemical artificial life in the form of a dynamic, chemical droplet. The robot uses computer vision, a self-organizing map, and a learning program to automatically categorize the behavior of the droplet that it creates. The robot can then use this categorization to autonomously detect the current state of the droplet and respond. The robot is programmed to visually track the droplet and either inject more chemical fuel to sustain a motile state or introduce a new chemical component that results in a state change (e.g., division). Coupling inexpensive open source hardware with sensing and feedback allows for replicable real-time manipulation and monitoring of nonequilibrium systems that would be otherwise tedious, expensive, and error-prone. This system is a first step towards the practical confluence of chemical, artificial intelligence, and robotic approaches to artificial life.

A modular robot consists of a set of mechatronic modules that can be connected in many different ways, which makes it possible to build robots of many different shapes from the same basic set of modules. The main contribution of this work is an algorithm that, given the parameters of a module and the number of modules, efficiently can calculate how many different configurations and shapes can be built. These numbers are important because the first is a measure of the self-reconfigurability and the second, given there is a relationship between form and function, the versatility of a modular robot. As an experimental contribution, we enumerate the configuration and shape spaces of square, two-dimensional modules with all possible connector configurations. We proceed to three dimensions and enumerate the spaces of the theoretically interesting sliding cube module, and the M-TRAN and SuperBot modules. Several observations are made, an important one is that the shape spaces of the two physical modules are large even for a small number of modules (103 different shapes using 3 modules). This implies that it is not the lack of shape diversity that holds these modular robots back from being versatile. A result that suggests that if modules are designed right, versatility can potentially be reached even with few modules, which is contrary to the common belief in the community that more is better.

The distinctive feature of modular robots consists in their reconfigurable mechanical structure, as they are assembled on-demand from basic mechatronic units. This implies that kinematic models of the robots need to be computed on a case-by-case basis for each specific assembly, which is a manual and hence time-consuming and error-prone procedure. We propose to automate this process by automatically computing such kinematic models starting from simple descriptions of the modules and their assemblies. This automated computation is supported by our toolchain for programming arbitrary modular robots in arbitrary configurations, presented in this paper. We contribute two novel results through this approach. First, a high-level programming language that provides kinematic abstractions for arbitrary modular robots, in contrast to the robot-specific solutions currently available. Second, a programming abstraction to subsume multiple kinematically equivalent robot assemblies into a so-called kinematic configuration, hence eliminating the need to explicitly enumerate and program each of them. These contributions advance current techniques for modular robot programming by demonstrating a tool that a) targets multiple mechanical platforms, offering the first general solution for modular robot programming, and b) raises the abstraction level by allowing users to reason and program in terms of standardized kinematic models that are automatically mapped to physical robot configurations by the toolchain.

We explore vacuum as bonding force for modular robotics. Vacuubes are a set of modules that propagate vacuum across their interfaces in order to generate adhesive forces to form and hold structures. We use analog circuits to simulate vacuum transients and understand critical design parameters and then validate these insights in experiments. A 49-module structure that employs vacuum bonding is demonstrated. We conclude that vacuum offers a relatively strong, simple, reliable, and power-efficient connection principle.

Modular robots are mechatronic devices that enable the construction of highly versatile and flexible robotic systems whose mechanical structure can be dynamically modified. The key feature that enables this dynamic modification is the capability of the individual modules to connect to each other in multiple ways and thus generate a number of different mechanical systems, in contrast with the monolithics fixed structure of conventional robots. The mechatronic flexibility, however, complicates the development of models and programming abstractions for modular robots, since manually describing and enumerating the full set of possible interconnections is tedious and error-prone for real-world robots. In order to allow for a general formulation of spatial abstractions for modular robots and to ensure correct and streamlined generation of code dependent on mechanical properties, we have developed the Modular Mechatronics Modelling Language (M3L). M3L is a domain-specific language, which can model the kinematic structure of individual robot modules and declaratively describe their possible interconnections rather than requiring the user to enumerate them in their entirety. From this description, the M3L compiler generates the code that is needed to simulate the resulting robots within Webots, widely used commercial robot simulator, and the software component needed for spatial structure computations by a virtual machine-based runtime system, which we have developed and used for programming physical modular robots

In this paper we study online gait optimization for modular robots. The learning strategy we apply is distributed, independent on robot morphology, and easy to implement. First we demonstrate how the strategy allows an ATRON robot to adapt to faults and changes in its morphology and we study the strategy's scalability. Second we extend the strategy to learn the parameters of gait-tables for ATRON and M-TRAN robots. We conclude that the presented strategy is effective for online learning of gaits for most types of modular robots and that learning can effectively be distributed by having independent processes learning in parallel.

Modular robot programming spans a number of issues ranging from high-level coordination to controller distribution and update in individual modules. The latter issue has received little attention from the research community though in our experience it is one of the main factors hindering agile development and experimentation with physical robots: reprogramming tens or hundreds of modules can be a major overhead in the development process and cannot be done with traditional approaches without restarting the robot, which impedes updating a running system. We propose a solution based on a virtual machine design shaped around three core concepts: the context of a module and its role in the ensemble, the reactive nature of robot controllers, and control programs decomposable into subparts that can be dynamically and separately redefined. We show that by incorporating those concepts into the design we are able to both achieve program conciseness (thus providing fast and efficient code distribution) and program expressiveness (thus providing versatility to represent diverse control algorithms). The virtual machine is programmed in a high-level role-oriented language that allows the programmer to declaratively specify how programs are deployed in the modular robot. Our approach enables fast and incremental on-line updates, allowing the programmer to interactively experiment with the physical robots. We show how this design lends itself to an efficient implementation targeting typical resource-constrained modular robotic hardware by illustrating our prototype implementation for the ATRON self-reconfigurable robot.

This paper describes the electronics and communication system of Odin, a novel heterogeneous modular robot made of links and joints. The electronics is divided into two printed circuit boards: a General board with reusable components and a Specific board with non-reusable components. While the General board is common to the design of every type of module, such as power, actuator, sensor and structure, the Specific board is unique to each type of module. The communication system, one of the most important reusable components of Odin, is based on local buses that can be extended by bridging electrical signals.

As exchange of information is essential to modular robots, deciding between local or global communication is a common design choice. This choice, however, still lacks theoretical support.

To understand the capabilities and behavior of a robot it is important to have knowledge about its physical structure and how its actuators control its shape. In this paper we analyze the kinematics and develop a general representation of a configuration of the heterogeneous modular robot Odin. The basics of estimating the shape of the Odin robot is presented, which leads the way for further research on the Odin robot and similar robots. We present an example of how to represent and estimate the shape of a tetrahedron configuration with various types of modules. We conclude that this representation can be used to find the physical constraints of the Odin robot and estimate the shape of a configuration.

Modular, self-reconfigurable robots are robots that can change their own shape by physically rearranging the modules from which they are built. Self-reconfiguration can be controlled by e.g. an off-line planner, but numerous implementation issues hamper the actual self-reconfiguration process: the continuous evolution of the communication topology increases the risk of communications failure, generating code that correctly controls the self-reconfiguration process is non-trivial, and hand-tuning the self-reconfiguration process is tedious and error-prone. To address these issues, we have developed a distributed scripting language that controls self-reconfiguration of the ATRON robot using a robust communication scheme that relies on local broadcast of shared state. This language can be used as the target of a planner, offers direct support for parallelization of independent operations while maintaining correct sequentiality of dependent operations, and compiles to a robust and efficient implementation. Moreover, a novel feature of this language is its reversibility: once a self-reconfiguration sequence is described the reverse sequence is automatically available to the programmer, significantly reducing the amount of work needed to deploy self-reconfiguration in larger scenarios. We demonstrate our approach with long-running (reversible) self-reconfiguration experiments using the ATRON robot and a reversible self-reconfiguration experiment using simulated MTRAN modules.

Generic simulation platforms such as player/stage are an essential tool in mobile robotics, but until now no similar platforms have been available for the field of self-reconfigurable robots. We here present a generic simulation platform for modular, self-reconfigurable robots: the unified simulator for self-reconfigurable robots (USSR). USSR is based on a physics engine, allowing simulation of both self-reconfiguration and dynamic interaction with the environment. The simulator is implemented as a framework that provides numerous components that can be combined to form new or existing modular robots, allowing easy experimentation: USSR currently includes support for the ATRON, Odin, and M-TRAN modular robots.

Highly sophisticated animals consist of a set of heterogenous modules decided by nature so that they can survive in a complex environment. In this paper we present a new modular robot inspired by biology called Odin. The Odin robot is based on a deformable lattice and consists of an extendable set of heterogeneous modules. We present the design and implementation of a cubic closed-packed (CCP) joint module, a telescoping link, and a flexible connection mechanism. The developed robot is highly versatile and opens up for a wide range of new research in modular robotics.

The ATRON self-reconfigurable robot consists of simple one degree of freedom ATRON modules. The motion capabilities of an individual module are therefore quite limited. To compensate for this, meta-modules composed of more than one module are used to shape-change the system. Meta-modules emerge from the environment created by other modules, move on the surface of other modules and stop at a new position. The flow of meta-modules, from one place to another on the structure of modules, realizes the desired self-reconfiguration. In this paper we compare six different meta-module types composed of ATRON modules. Variations of meta-module morphology and meta-actions are investigated for its ability to shape-change the robot. We conclude that two of the investigated meta-module types are able to shape-change the robot to an acceptable extent

The Deformatron robot is a homogeneous, modular robot. The Deformatron modules can play one of three roles in the physical structure of the robot: bone, tendon, and muscle. These roles are inspired by their biological counterparts. This combination of roles gives us a modular robot which potentially may have enough structural strength and actuation power to manipulate its environment and work in three dimension even in the presence of gravity. In this paper we present our preliminary LEGO-based prototype of the Deformatron robot. We measure and analyse the properties of the system in terms of structural strength and actuation power. Finally, we conclude that the Deformatron concept may provide an avenue of research which may make modular robots stronger and better suited for real world task environments

Self-reconfigurable robots are built from modules, which are autonomously able to change the way they are connected, thus changing the overall shape of the robot. This self-reconfiguration process is difficult to control, because it involves the distributed coordination of large numbers of identical modules connected in time-varying ways. We present an approach where a desired shape is grown based on a scalable representation of the desired configuration, which is automatically generated from a 3D CAD model. The size of the configuration is adjusted continually to match the number of modules in the system. This has the advantage that if modules are removed or added, the system automatically adjusts its scale and thus self-repair is obtained as a side effect. This capability is achieved by distributed, local rules for module movement that are independent of the goal configuration. We compare the scale independent approach to one where the desired configuration is grown directly at a fixed scale. We find that the features of the scale independent approach come at the expense of an increased number of moves, messages, and time steps taken to reconfigure.

In this paper we examine locomotion in the context of self-reconfigurable robots. Self-reconfigurable robots are robots built from many connected modules. A self-reconfigurable robot can change its shape and configuration by changing the way these modules are connected. The focus of this paper is to understand how several locomotion gaits can be represented in such a robot and how the robot can select one of these gaits depending on its configuration. We implement a control system based on role based control in a physical self-reconfigurable robot built from seven modules. In several experiments we successfully demonstrate that when the robot is manually reconfigured from a chain to a quadruped configuration the robot changes gait from a sidewinder snake gait to a quadruped walking gait. We conclude that role based control is a promising central method for controlling locomotion of self-reconfigurable robots.

We describe a team of robots that uses a trail of landmarks to navigate between places of interest. The landmarks are not physical; they are waypoint coordinates generated online by each robot and shared with teammates over the network. Waypoints are specified with reference to features in the world that are relevant to the team's task and common to all robots. Using such task-level features as landmarks avoids the need to sense and name physical landmarks. Using these common landmarks, each robot can transform waypoint coordinates into its local reference frame, avoiding the cost of maintaining a fixed global coordinate system. The algorithm is tested in an experiment in which a team of 4 autonomous mobile robots run in our office building for more than 3 hours, travelling a total of 8.2 km (5.1 miles). Despite significant divergence of their local coordinate systems, they are able to share waypoints, forming and following a common trail between two fixed locations.

We describe localization-space trails (LOST), a method that enables a team of robots to navigate between places of interest in an initially unknown environment using a trail of landmarks. The landmarks are not physical; they are waypoint coordinates generated online by each robot and shared with teammates. Waypoints are specified in each robot's local coordinate system, and contain references to features in the world that are relevant to the team's task and common to all robots. Using these task-level references, robots can share waypoints without maintaining a global coordinate system. The method is tested in a series of real-world multirobot experiments. The results demonstrate that the method: 1) copes with accumulating odometry error; 2) is robust to the failure of individual robots; 3) converges to the best route discovered by any robot in the team. In one experiment, a team of four autonomous mobile robots performs a resource transportation task in our uninstrumented office building. Despite significant divergence of their local coordinate systems, the robots are able to share waypoints, forming and following a common trail between two predetermined locations for more than three hours, traveling a total of 8.2 km (5.1 miles) before running out of power. Designed to scale to large populations, LOST is fully distributed, with low costs in processing, memory, and bandwidth. It combines metric data about the position of features in the world with instructions on how to get from one place to another; producing something between a map and a plan.

Successful distributed sensing and control require data to flow effectively between sensors, processors and actuators on single robots, in groups and across the Internet. We propose a mechanism for achieving this flow that we have found to be powerful and easy to use; we call it Player. Player combines an efficient message protocol with a simple device model. It is implemented as a multithreaded TCP socket server that provides transparent network access to a collection of sensors and actuators, often comprising a robot. The socket abstraction enables platform- and language-independent control of these devices, allowing the system designer to use the best tool for the task at hand Player is freely available from http://robotics.usc.edu/player.