An Institutional Robotics Approach to the Design of Socially Aware Multi-Robot Behaviors

We propose an institutional robotics approachto the design of socially-aware multi-robot systems, wherecooperation among the robots and their social interactions withhumans are guided using institutions. Inspired by the conceptsstemming from economical sciences, robot institutions serve ascoordination artifacts, which specify behavioral rules that areacceptable or desirable given the situation and which can bereplaced by other rules to enforce new acceptable or desirablebehaviors without changing the robot’s core code. In this paperwe propose a formal methodology for consistent design ofcoordinated multi-robot behaviors intended for use in human-populated environments. We illustrate theoretical concepts withpractical examples. Graph-based formations serve as a basisfor coordinated multi-robot behaviors and concepts from theliterature on human-aware navigation provide social rules thatare enforced by the institutions. Experiments are carried outin a high-fidelity robotic simulator to illustrate the applicationof the theoretical concepts.

Social-Aware Coordination of Multi-robot Systems Based on Institutions

José Nuno Ferreira Maia Pereira, Alcherio Martinoli

Institutional robotics (IR) is an approach to the coordination of multi-robot systems that draws inspiration from social sciences, namely from institutional economics. Using the concept of institution, it aims to provide a comprehensive strategy for specifying social interactions (e.g., norms, roles, hierarchies) among robots. In previous work, we have introduced a control methodology for multi-robot systems that takes into account institutions in order to create an Institutional Agent Controller (IAC) that captures such social interactions. In this chapter, the IAC design methodology is validated in a case study concerned with a swarm of 40 real, resource-constrained robots which has to maintain wireless connectivity. We then investigate a second case study dealing with more complex social interactions, showing that institutional roles can effectively help a multi-robot system to coordinate and improve performance in a given task of social nature. Given the fact that institutions are one of the tools in use within human societies to shape social interactions, our intuition is that IR can also facilitate coordination with humans in scenarios involving many-to-many human–robot interactions. We discuss how the IR concepts and the IAC design methodology can be implemented in real-world scenarios where multiple robots must interact with multiple humans in a socially aware manner.

Springer International Publishing2014

Behaviour design in microrobots

Masoud Asadpour

In order to verify models of collective behaviors of animals, robots could be manipulated to implement the model and interact with real animals in a mixed-society. This thesis describes design of the behavioral hierarchy of a miniature robot, that is able to interact with cockroaches, and participates in their collective decision makings. The robots are controlled via a hierarchical behavior-based controller in which, more complex behaviors are built by combining simpler behaviors through fusion and arbitration mechanisms. The experiments in the mixed-society confirms the similarity between the collective patterns of the mixed-society and those of the real society. Moreover, the robots are able to induce new collective patterns by modulation of some behavioral parameters. Difficulties in the manual extraction of the behavioral hierarchy and inability to revise it, direct us to benefit from machine learning techniques, in order to devise the composition hierarchy and coordination in an automated way. We derive a Compact Q-Learning method for micro-robots with processing and memory constraints, and try to learn behavior coordination through it. The behavior composition part is still done manually. However, the problem of the curse of dimensionality makes incorporation of this kind of flat-learning techniques unsuitable. Even though optimizing them could temporarily speed up the learning process and widen their range of applications, their scalability to real world applications remains under question. In the next steps, we apply hierarchical learning techniques to automate both behavior coordination and composition parts. In some situations, many features of the state space might be irrelevant to what the robot currently learns. Abstracting these features and discovering the hierarchy among them can help the robot learn the behavioral hierarchy faster. We formalize the automatic state abstraction problem with different heuristics, and derive three new splitting criteria that adapt decision tree learning techniques to state abstraction. Proof of performance is supported by strong evidences from simulation results in deterministic and non-deterministic environments. Simulation results show encouraging enhancements in the required number of learning trials, robot's performance, size of the learned abstraction trees, and computation time of the algorithms. In the other hand, learning in a group provides free sources of knowledge that, if communicated, can broaden the scales of learning, both temporally and spatially. We present two approaches to combine output or structure of abstraction trees. The trees are stored in different RL robots in a multi-robot system, or in the trees learned by the same robot but using different methods. Simulation results in a non-deterministic football learning task provide strong evidences for enhancement in convergence rate and policy performance, specially in heterogeneous cooperations.

EPFL2006

Communication in a Swarm of Miniature Robots: The e-Puck as an Educational Tool for Swarm Robotics

Christopher Cianci, Alcherio Martinoli, James Pugh, Xavier Raemy

Swarm intelligence, and swarm robotics in particular, are reaching a point where leveraging the potential of communication within an artificial systempromises to uncover newand varied directions for interesting research without compromising the key properties of swarmintelligent systems such as self-organization, scalability, and robustness. However, the physical constraints of using radios in a robotic swarm are hardly obvious, and the intuitive models often used for describing such systems do not always capture them with adequate accuracy. In order to demonstrate this effectively in the classroom, certain tools can be used, including simulation and real robots. Most instructors currently focus on simulation, as it requires significantly less investment of time, money, and maintenance—but to really understand the differences between simulation and reality, it is also necessary to work with the real platforms from time to time. To our knowledge, our coursemay be the only one in the world where individual students are consistently afforded the opportunity to work with a networked multi-robot system on a tabletop. The e-Puck,1 a low-cost small-scale mobile robotic platform designed for educational use, allows us bringing real robotic hardware into the classroom in numbers sufficient to demonstrate and teach swarm-robotic concepts.We present here a custom module for local radio communication as a stackable extension board for the e-Puck, enabling information exchange between robots and also with any other IEEE 802.15.4-compatible devices. Transmission power can be modified in software to yield effective communication ranges as small as fifteen centimeters. This intentionally small range allows us to demonstrate interesting collective behavior based on local information and control in a limited amount of physical space, where ordinary radios would typically result in a completely connected network. Here we show the use of this module facilitating a collective decision among a group of 10 robots.