Scalable Co-Evolution of Soft Robot Properties and Gaits

The field of soft robotics is very promising; applications in-clude urban search and rescue and covert surveillance, but these projects are not yet realized, partly because of the difficulties in soft robot shape and locomotion design. Be-cause of this, traditional design methods do not prove to be effective. This project attempts to come up with solu-tions to this soft robot design problem; utilizing a genetic algorithm, a computer simulation of Darwin’s “Survival of the Fittest,” this project attempts to make soft bodies move. This genetic algorithm evaluates each solution in simulation, and assigns each one a fitness based on distance travelled. Furthermore, this project implements a technique called co-evolution, which evolves two different things in lockstep, uti-lizing new found advancements in one to help bolster the other. This project evolves soft bodies’ physical properties, values that affect how they move, alongside locomotion tech-niques, the gaits defining their movement. Optimizations to this process are realized in the use of scalable soft meshes; this system starts on a simple mesh, and slowly increases its density, reducing the overall computation time

Evolvability signatures of generative encodings: beyond standard performance benchmarks

Evolutionary robotics is a promising approach to autonomously synthesize machines with abilities that resemble those of animals, but the field suffers from a lack of strong foundations. In particular, evolutionary systems are currently assessed solely by the fitness score their evolved artifacts can achieve for a specific task, whereas such fitness-based comparisons provide limited insights about how the same system would evaluate on different tasks, and its adaptive capabilities to respond to changes in fitness (e.g., from damages to the machine, or in new situations). To counter these limitations, we introduce the concept of "evolvability signatures", which picture the post-mutation statistical distribution of both behavior diversity (how different are the robot behaviors after a mutation?) and fitness values (how different is the fitness after a mutation?). We tested the relevance of this concept by evolving controllers for hexapod robot locomotion using five different genotype-to-phenotype mappings (direct encoding, generative encoding of open-loop and closed-loop central pattern generators, generative encoding of neural networks, and single-unit pattern generators (SUPG)). We observed a predictive relationship between the evolvability signature of each encoding and the number of generations required by hexapods to adapt from incurred damages. Our study also reveals that, across the five investigated encodings, the SUPG scheme achieved the best evolvability signature, and was always foremost in recovering an effective gait following robot damages. Overall, our evolvability signatures neatly complement existing task-performance benchmarks, and pave the way for stronger foundations for research in evolutionary robotics.Comment: 24 pages with 12 figures in the main text, and 4 supplementary figures. Accepted at Information Sciences journal (in press). Supplemental videos are available online at, see http://goo.gl/uyY1R

Exploring the effects of robotic design on learning and neural control

The ongoing deep learning revolution has allowed computers to outclass humans in various games and perceive features imperceptible to humans during classification tasks. Current machine learning techniques have clearly distinguished themselves in specialized tasks. However, we have yet to see robots capable of performing multiple tasks at an expert level. Most work in this field is focused on the development of more sophisticated learning algorithms for a robot's controller given a largely static and presupposed robotic design. By focusing on the development of robotic bodies, rather than neural controllers, I have discovered that robots can be designed such that they overcome many of the current pitfalls encountered by neural controllers in multitask settings. Through this discovery, I also present novel metrics to explicitly measure the learning ability of a robotic design and its resistance to common problems such as catastrophic interference. Traditionally, the physical robot design requires human engineers to plan every aspect of the system, which is expensive and often relies on human intuition. In contrast, within the field of evolutionary robotics, evolutionary algorithms are used to automatically create optimized designs, however, such designs are often still limited in their ability to perform in a multitask setting. The metrics created and presented here give a novel path to automated design that allow evolved robots to synergize with their controller to improve the computational efficiency of their learning while overcoming catastrophic interference. Overall, this dissertation intimates the ability to automatically design robots that are more general purpose than current robots and that can perform various tasks while requiring less computation.Comment: arXiv admin note: text overlap with arXiv:2008.0639

Evolving soft locomotion in aquatic and terrestrial environments: effects of material properties and environmental transitions

Designing soft robots poses considerable challenges: automated design approaches may be particularly appealing in this field, as they promise to optimize complex multi-material machines with very little or no human intervention. Evolutionary soft robotics is concerned with the application of optimization algorithms inspired by natural evolution in order to let soft robots (both morphologies and controllers) spontaneously evolve within physically-realistic simulated environments, figuring out how to satisfy a set of objectives defined by human designers. In this paper a powerful evolutionary system is put in place in order to perform a broad investigation on the free-form evolution of walking and swimming soft robots in different environments. Three sets of experiments are reported, tackling different aspects of the evolution of soft locomotion. The first two sets explore the effects of different material properties on the evolution of terrestrial and aquatic soft locomotion: particularly, we show how different materials lead to the evolution of different morphologies, behaviors, and energy-performance tradeoffs. It is found that within our simplified physics world stiffer robots evolve more sophisticated and effective gaits and morphologies on land, while softer ones tend to perform better in water. The third set of experiments starts investigating the effect and potential benefits of major environmental transitions (land - water) during evolution. Results provide interesting morphological exaptation phenomena, and point out a potential asymmetry between land-water and water-land transitions: while the first type of transition appears to be detrimental, the second one seems to have some beneficial effects.Comment: 37 pages, 22 figures, currently under review (journal

Robots as Powerful Allies for the Study of Embodied Cognition from the Bottom Up

A large body of compelling evidence has been accumulated demonstrating that embodiment – the agent’s physical setup, including its shape, materials, sensors and actuators – is constitutive for any form of cognition and as a consequence, models of cognition need to be embodied. In contrast to methods from empirical sciences to study cognition, robots can be freely manipulated and virtually all key variables of their embodiment and control programs can be systematically varied. As such, they provide an extremely powerful tool of investigation. We present a robotic bottom-up or developmental approach, focusing on three stages: (a) low-level behaviors like walking and reflexes, (b) learning regularities in sensorimotor spaces, and (c) human-like cognition. We also show that robotic based research is not only a productive path to deepening our understanding of cognition, but that robots can strongly benefit from human-like cognition in order to become more autonomous, robust, resilient, and safe

Soft Scalable Self-Reconfigurable Modular Cellbot

Hazardous environments such as disaster affected areas, outer space, and radiation affected areas are dangerous for humans. Autonomous systems which can navigate through these environments would reduce risk of life. The terrains in these applications are diverse and unknown, hence there is a requirement for a robot which can self-adapt its morphology and use suitable control to optimally move in the desired manner. Although there exist monolithic robots for some of these applications, such as the Curiosity rover for Mars exploration, a modular robot containing multiple simple units could increase the fault tolerance. A modular design also enables scaling up or down of the robot based on the current task, for example, scaling up by connecting multiple units to cover a wider area or scaling down to pass through a tight space.Taking bio-inspiration from cells, where – based on environmental conditions – cells come together to form different structures to carry out different tasks, a soft modular robot called Cellbot was developed which was composed of multiple units called ‘cells’. Tests were conducted to understand the cellbot movement over different frictional surfaces for different actuation functions, the number of cells connected in a line (1D), and the shapes formed by connecting cells in 2D. A simulation model was developed to test a large range of frictional values and actuation functions for different friction coefficients. Based on the obtained results, cells could be designed using a material with frictional properties lying in the optimal locomotion range. In other cases, where the application has diverse terrains, the number of connected units can be changed to optimise the robot locomotion. Initial tests were conducted using a ‘ball robot’, where the cellbot was designed using balls which touch ground to exploit friction and actuators to provide force to move the robot. The model was extended to develop, a ‘bellow robot’ which was fabricated using hyper-elastic bellows and employed pneumatic actuation. The amount of inflation of a cell and its neighbouring cells determined if the cell would touch the ground or be lifted up. This was used to change cell behaviour where a cell could be touching ground to provide anchoring friction, or lifted to push or pull the cells and thereby move the robot. The cells were connected by magnets which could be disconnected and reconnected by morphing the robot body. The cellbot can thus reconfigure by changing the number of connected units or its shape. The easy detachment can be used to remove and replace damaged cells. Complex cellbot movements can be achieved by either switching between different robot morphologies or by changing actuation control.Future cellbots will be controlled remotely to change their morphology, control, and number of connected cells, making them suitable for missions which require fault tolerance and autonomous shape adaptation. The proposed cellbot platform has the potential to reduce the energy, time and costs in comparison to traditional robots and has potential for applications such as exploration missions for outer space, search and rescue missions for disaster affected areas, internal medical procedures, and nuclear decommissioning.<br/

Challenges in the Locomotion of Self-Reconfigurable Modular Robots

Self-Reconfigurable Modular Robots (SRMRs) are assemblies of autonomous robotic units, referred to as modules, joined together using active connection mechanisms. By changing the connectivity of these modules, SRMRs are able to deliberately change their own shape in order to adapt to new environmental circumstances. One of the main motivations for the development of SRMRs is that conventional robots are limited in their capabilities by their morphology. The promise of the field of self-reconfigurable modular robotics is to design robots that are robust, self-healing, versatile, multi-purpose, and inexpensive. Despite significant efforts by numerous research groups worldwide, the potential advantages of SRMRs have yet to be realized. A high number of degrees of freedom and connectors make SRMRs more versatile, but also more complex both in terms of mechanical design and control algorithms. Scalability issues affect these robots in terms of hardware, low-level control, and high-level planning. In this thesis we identify and target three major challenges: (i) Hardware design; (ii) Planning and control; and, (iii) Application challenges. To tackle the hardware challenges we redesigned and manufactured the Self-Reconfigurable Modular Robot Roombots to meet desired requirements and characteristics. We explored in detail and improved two major mechanical components of an SRMR: the actuation and the connection mechanisms. We also analyzed the use of compliant extensions to increase locomotion performance in terms of locomotion speed and power consumption. We contributed to the control challenge by developing new methods that allow an arbitrary SRMR structure to learn to locomote in an efficient way. We defined a novel bio-inspired locomotion-learning framework that allows the quick and reliable optimization of new gaits after a morphological change due to self-reconfiguration or human construction. In order to find new suitable application scenarios for SRMRs we envision the use of Roombots modules to create Self-Reconfigurable Robotic Furniture. As a first step towards this vision, we explored the use and control of Plug-n-Play Robotic Elements that can augment existing pieces of furniture and create new functionalities in a household to improve quality of life