Frank Veenstra

Etor Arza, Frank Veenstra, Tønnes F. Nygaard et al.

The design (shape) of a robot is usually decided before the control is implemented. This might limit how well the design is adapted to a task, as the suitability of the design is given by how well the robot performs in the task, which requires both a design and a controller. The co-optimization or simultaneous optimization of the design and control of robots addresses this limitation by producing a design and control that are both adapted to the task. This paper investigates some of the challenges inherent in the co-optimization of design and control in simulation. The results show that reducing how well the controllers are trained during the co-optimization process significantly improves the robot's performance when considering a second phase in which the controller for the best design is retrained with additional resources. In addition, the results demonstrate that the computation budget allocated to training the controller for each design influences design complexity, with simpler designs associated with lower training budgets. This paper experimentally studies key questions discussed in other works in the literature on the co-optimization of design and control of robots in simulation in four different co-optimization problems.

Jørgen Nordmoen, Frank Veenstra, Kai Olav Ellefsen et al.

In modular robotics, modules can be reconfigured to change the morphology of the robot, making it able to adapt for specific tasks. However, optimizing both the body and control is a difficult challenge due to the intricate relationship between fine-tuning control and morphological changes that can invalidate such optimizations. To solve this challenge we compare three different Evolutionary Algorithms on their capacity to optimize morphologies in modular robotics. We compare two objective-based search algorithms, with MAP-Elites. To understand the benefit of diversity we transition the evolved populations into two difficult environments to see if diversity can have an impact on solving complex environments. In addition, we analyse the genealogical ancestry to shed light on the notion of stepping stones as key to enable high performance. The results show that MAP-Elites is capable of evolving the highest performing solutions in addition to generating the largest morphological diversity. For the transition between environments the results show that MAP-Elites is better at regaining performance by promoting morphological diversity. With the analysis of genealogical ancestry we show that MAP-Elites produces more diverse and higher performing stepping stones than the other objective-based search algorithms. Transitioning the populations to more difficult environments show the utility of morphological diversity, while the analysis of stepping stones show a strong correlation between diversity of ancestry and maximum performance on the locomotion task. The paper shows the advantage of promoting diversity for solving a locomotion task in different environments for modular robotics. By showing that the quality and diversity of stepping stones in Evolutionary Algorithms is an important factor for overall performance we have opened up a new area of analysis and results.

Jørgen Nordmoen, Frank Veenstra, Kai Olav Ellefsen et al.

In Evolutionary Robotics a population of solutions is evolved to optimize robots that solve a given task. However, in traditional Evolutionary Algorithms, the population of solutions tends to converge to local optima when the problem is complex or the search space is large, a problem known as premature convergence. Quality Diversity algorithms try to overcome premature convergence by introducing additional measures that reward solutions for being different while not necessarily performing better. In this paper we compare a single objective Evolutionary Algorithm with two diversity promoting search algorithms; a Multi-Objective Evolutionary Algorithm and MAP-Elites a Quality Diversity algorithm, for the difficult problem of evolving control and morphology in modular robotics. We compare their ability to produce high performing solutions, in addition to analyze the evolved morphological diversity. The results show that all three search algorithms are capable of evolving high performing individuals. However, the Quality Diversity algorithm is better adept at filling all niches with high-performing solutions. This confirms that Quality Diversity algorithms are well suited for evolving modular robots and can be an important means of generating repertoires of high performing solutions that can be exploited both at design- and runtime.

Heiko Hamann, Mohammad Divband Soorati, Mary Katherine Heinrich et al.

Key to our project flora robotica is the idea of creating a bio-hybrid system of tightly coupled natural plants and distributed robots to grow architectural artifacts and spaces. Our motivation with this ground research project is to lay a principled foundation towards the design and implementation of living architectural systems that provide functionalities beyond those of orthodox building practice, such as self-repair, material accumulation and self-organization. Plants and robots work together to create a living organism that is inhabited by human beings. User-defined design objectives help to steer the directional growth of the plants, but also the system's interactions with its inhabitants determine locations where growth is prohibited or desired (e.g., partitions, windows, occupiable space). We report our plant species selection process and aspects of living architecture. A leitmotif of our project is the rich concept of braiding: braids are produced by robots from continuous material and serve as both scaffolds and initial architectural artifacts before plants take over and grow the desired architecture. We use light and hormones as attraction stimuli and far-red light as repelling stimulus to influence the plants. Applied sensors range from simple proximity sensing to detect the presence of plants to sophisticated sensing technology, such as electrophysiology and measurements of sap flow. We conclude by discussing our anticipated final demonstrator that integrates key features of flora robotica, such as the continuous growth process of architectural artifacts and self-repair of living architecture.