Multi-Competence Optimization and Adaptation

in Robotics and A-life



September 22, 2008

Acropolis Convention Center

 Nice, France


In conjunction with

IEEE/RSJ Int. Conf. on Intelligent Robots and Systems

IROS 2008



Workshop Organizers:

Amiram (Ami) Moshaiov, Tel Aviv University, Israel

Hussein Abbass, University of New South Wales, Australia


Contact: For further information please contact [moshaiov AT]


Workshop Theme:

Multi-Competence Optimization and Adaptation in Robotics and A-life

NOTE - We use the term multi-competence interchangeably with multi-objective to encompass both natural and artificial adaptation processes.


Workshop Main Goals:

The main goals of the IROS-MOR workshop series are: 1. To serve as a meeting place for researchers dealing with various multi-objective optimization problems in robotics, 2. To inform researchers in robotics about the state-of-the-art in multi-objective optimization methods, 3. To explore the similarities and dissimilarities between bio-inspired and bio-plausible multi-competence adaptation, as related to robotics and A-life.



The 1st Multi-Objective Robotics (MOR) workshop took place at IROS-2006. It focused on the 1st and 2nd goals of the IROS-MOR workshop series, namely to deal with Multi-Objective Problems (MOPs) in robotics and the state-of-the-art methods to achieve it. As detailed at , the 2006 meeting aimed at contributions in Multi-objective Design (MOD) of Robot Mechanics, MOD of Robot Control, MOD of Mixed (Simultaneous MOD of) Mechanics and Control of Robots, Multi-objective Evolutionary Robotics, Multi-objective Path Planning, Multi-objective Obstacle Avoidance, Multi-objective Sensing (Attention), Multi-objective Manipulation and Grasping, Interactive MOR, Robust MOR, Multi-objective Learning and Adaptation, Multi-objective Agents (MOA), Concept-based MOP in Robotics, MOP in Application Areas of Robotics, and other related areas.

In 2008, we plan to extend the scope of IROS-MOR to also include A-life related research. The background for this 3rd goal is given below.  

Comparing nature and the artificial has been proven to be fruitful for bio-inspired technical systems and methods, as well as for scientific studies of nature. Well known examples of the former are bio-inspired robotics and soft computing methods, whereas the use of bio-morphs to study "the blind watchmaker" is a renowned example of the latter. Here, the focus of comparison is on tradeoffs. Despite of the notions of 'fitness' and 'performance' often being considered closely related, the idea of 'Pareto-front,' which helps engineers to investigate tradeoffs among design objectives, appear strange or irrelevant to most biologists. Yet, tradeoffs are neither new to biologists, nor to cognitive scientists and are often being considered in a weighted sum approach in such related studies.

Bio-inspired and bio-plausible design may be particularly useful in attempts to understand the nature of evolution tradeoffs and the degree to which evolution involves a "balance" between selection for multiple "objectives." Or, in more general terms, bio-inspired and bio-plausible design, sensing, learning, and decision making, could support the study of multi-competence adaptation in natural and artificial systems. This, in turns, may help developing robust robots that are adaptable to changing conditions. Evolution, learning, and development influence adaptation in different time scales and forms. Hence we seek to understand the role of tradeoffs in such individual modes of adaptation as well as in their combined adaptation effects (as in ENN), in both robotics and A-life. We further want to explore, which are the most fundamental tradeoffs in natural adaptation, and in engineering design that could strongly influence the design of multi-competence cognitive robots.



·        Tradeoffs and multi-objective optimization in robotics including: tradeoffs in robot mechanical design, sensing, control, behavior, planning, learning, and development (see also detailed list at the first paragraph of the background above).

·        Handling tradeoffs in A-life research including: tradeoffs in sensing, control, behavior, planning, learning, and  development

·        Tradeoffs in co-evolution and in game theory with applications to robotics and A-life

·        Tradeoffs in related application areas such as: Engineering Design, Control, Mechatronics, Informatics, Multi-Criteria Decision Making, Complexity Science, Biological Systems, Biomechanics, Environmental Planning, Economics, Social Sciences, Cognitive and Behavioral Sciences (see note below)

·        Tradeoffs in computational methods such as: Soft Computing, Organic Computing, Bio-inspired Hybrid Metaheuristics, and their applications to robotics and A-life (see note below)

·        Metaphors and analogies in evolutionary computations and bio-inspired hybrid-metaheuristics (as related to Multi-Objective Optimization, and to applications in robotics and A-life)

·        Similarities and dissimilarities between natural and artificial processes of adaptation and their "handling" of tradeoffs

·        The adaptation/optimization debate and its relation to teleology and the notion of objectives

·        Tradeoffs in natural systems and their comparison with tradeoffs in engineering design and in multi-criteria decision making

·         Tradeoffs in conceptual design and in natural "concepts" such as species and neural ensembles


Workshop Schedule:

9:00 -9:15
Introduction to the IROS-MOR workshop on  Multi-Competence Adaptation in Robotics and A-life
Amiram Moshaiov

9:15- 9:40
Integrated visual information processing on a humanoid robot with
foveated vision
Ales Ude

Incremental evolution using multi-objective evolutionary algorithms and
behavior sharing
Jean-Baptiste Mouret and St´ephane Doncieux

Exploration strategy based on multi-criteria decision making for an
autonomous mobile robot
Nicola Basilico and Francesco Amigoni

10:30 -10:50
Coffee break

10:50 - 11:15
Multi-objective multi-robot surveillance
Canu, Delle Fave, Iocchi, Ziparo

Evolutionary search of conceptual path plans using a
relaxed-Pareto-optimality approach
Elad Denenberg and Amiram Moshaiov

11:40- 12:30


Workshop Abstracts:


Integrated visual information processing on a humanoid robot

with foveated vision

Ales Ude

To successfully act in real-world environments, a robot must simultaneously perform a number of nontrivial visual tasks such as: 1. detecting objects and activities of interest, 2. observing them by actively controlling its eyes and body, and 3. analyzing the acquired images to extract new information about objects and events in the scene.  Humanoid vision systems that replicate the foveated structure of the human eye are well suited for these tasks because they allow simultaneous processing of visual information at different resolutions. Robots with foveated vision can monitor and explore their surroundings using wide-angle images of low resolution, thereby increasing the efficiency of the search process, while simultaneously extracting additional information – once the area of interest is determined – from narrow-angle but high resolution foveal images that contain more detail. In this talk I shall present our work on integrated processing of information acquired by peripheral and foveal vision and the associated motor control algorithms. The developed algorithms can be applied to solve multiple objectives at the same time, where the problems are as different as smooth pursuit and tracking on the one hand, and object recognition on the other hand.




Incremental evolution using multi-objective evolutionary algorithms and behavior sharing

Jean-Baptiste Mouret and St´ephane Doncieux


While many robot controllers have been successfully generated using evolutionary algorithms, current methods do not scale well to complex robotics tasks. The bootstrap problem is often recognized as one of the main causes of this difficulty: if all individuals from the first randomly generated population perform equally poorly, the evolutionary process won’t generate any interesting solution. In this paper, we introduce two approaches to overcome this problem, both of them based on multi-objective evolution. In the first method, called multi-subgoal evolution, the user defines a set of subgoals which could act as potential stepping stones to the final goal. We show how a multi-objective evolutionary algorithm can then be used to obtain working controllers. In the second proposed method, a distance between behaviors has to be designed. This distance is used to apply a selective pressure towards the most original solutions. These two processes are fully automatic: once the experiments have been defined, no further human intervention is required. These approaches have been successfully tested and compared on the evolution of a neuro-controller for a complex light-seeking mobile robot.




Exploration strategy based on multi-criteria decision making for an
autonomous mobile robot

Nicola Basilico and Francesco Amigoni


In robot mapping, exploration strategies are used to determine locations visited by an autonomous mobile robot in building a spatial representation of an environment. Usually, a next-best-view approach is used. The robot iteratively chooses the best location where to go among a set of candidates, evaluating each one of them on the basis of different criteria. For every candidate, utilities associated to single criteria are aggregated by means of ad hoc functions. The maximization of the aggregated utility greedily drives the robot’s choices. In this paper we propose an exploration strategy based on a multi-criteria decision making technique in which evaluation of candidate locations takes into account different criteria together with their mutual interactions. Our approach is more general than those proposed in literature and it’s independent of the nature and the number of criteria considered. Some preliminary experimental results are presented in order to assess the feasibility of our approach.





Multi-Objective Multi-Robot Surveillance

Canu, Delle Fave, Iocchi, Ziparo

In many surveillance applications, there are different properties of the environment to check. For example, in the case of robots surveillance at an industrial depot, one could be interested in verifying for fire alarms, intrusion alarms or bio-hazards. Given these different goals, it is very hard to characterize the solution of the multi-objective problem by defining a unique utility function. Indeed, this would require to define
a measure of the trade-off among objectives, which are, by definition, incommensurable quantities. In this paper, we present an approach to address such issues. In particular, we define the multi-robot multi-objective surveillance problem and show how this can be solved with a multi-objective heuristic search. The approach has been experimented by using an off-line planner and actual implementation on simulated and real multi-robot systems.


Evolutionary Search of Conceptual Path Plans

Using a Relaxed-Pareto-optimality Approach


Elad Denenberg and Amiram Moshaiov



This paper presents an evolutionary concept-based multi-objective optimization approach. A relaxed Pareto-optimality criterion is used to allow for conceptual plans, with performances close to those of the Pareto-optimal set, to survive the evolutionary search process. The proposed approach is significant to the final selection of conceptual and particular path plans. Such a selection may involve, in addition to the modeled objectives, post-run considerations such as robustness. Here, the relaxed problem is defined using a Pareto-epsilon measure. Furthermore, this paper provides a description of a novel multi-objective evolutionary algorithm that is tailored for the described problem. Finally, the algorithm is demonstrated using both an academic test function and a path planning problem.


Special Issue:

We plan to negotiate a related special issue in a leading journal to which a selection of the workshop accepted submissions will be considered.