HUMIES COMPETITION ENTRy DOCUMENT

Humies Application	Rolling Motion for a Tensegrity Robot	
Papers		•	Controlling Tensegrity Robots through Evolution Atil Iscen, Adrian Agogino, Vytas SunSpiral, Kagan Tumer GECCO 2013
	•	Learning to Control Complex Tensegrity Robots Atil Iscen, Adrian Agogino, Vytas SunSpiral, Kagan Tumer AAMAS 2013 (Extended Abstract) 	
Authors	Atil Iscen
atiliscen@gmail.com
849 Hayes St. San Francisco, CA, 94117, U.S.A
541-908-1978	
	Adrian Agogino
adrian.k.agogino@nasa.gov
MS 269-3, Moffett Field, CA 94035, USA
650-604-5985	
	Vytas Sunspiral
vytas.sunspiral@nasa.gov
MS 269-3, Moffett Field, CA 94035, USA
650-604-4363	
	Kagan Tumer
kagan.tumer@oregonstate.edu
204 Rogers Hall, Corvallis, OR 97331-6001, USA
(541) 737-9899 	
Corresponding Author	Atil Iscen
	
Criteria(s) applied to	(D) The result is publishable in its own right as a new scientific result 3/4 independent of the fact that the result was mechanically created.
(E) The result is equal to or better than a result that was considered an achievement in its field at the time it was first discovered.
	(A)	The result solves a problem of indisputable difficulty in its field.	
Criteria(s) applied to:
(Explanation)	Tensegrity robots are unique and potentially revolutionary mechanisms that are composed of purely compressive and tensile elements (rods and cables). These robots have the potential to be light-weight, energy efficient, robust to failures, flexible and compliant, enabling increased robot/environment interactions. Despite these advantages, tensegrity robots have not had any practical success until now due to their high non-linearity and oscillatory nature making them almost impossible to control. Our research has shown that evolution can solve this control problem, making dramatically better controllers than humans can. Human designers don’t know what sequence of deformations will provide viable motions. In addition, just to deform the structure to one specific shape, classical methods require solving 24 nonlinear equations for the simplest sphere shaped tensegrity structure.  These two aspects of the problem satisfies the criteria (F). As a consequence to this difficulty, part of literature contains studies about shapeshifting algorithms, and a little amount of work succeeded providing a rolling locomotion. These papers focus on the controls to provide slow static control resulting in one full revolution of the structure. In contrast, we provide dynamic efficient repeatedly rolling behavior taking full advantage of the oscillatory nature of the system. In this sense, our results satisfy the criteria (E). Moreover, the papers that we cite as previous work on this field do not provide results that are mechanically created by learning algorithms, this fact shows that our results by themselves are publishable independent of the fact that they are mechanically created, which satisfies the criteria (D).	
Full Citation of the Papers	A. Iscen, A. Agogino, V. SunSpiral, and K. Tumer. Controlling tensegrity robots through evolution. To Appear In Proceedings of Genetic and Evolutionary Computation Conference (GECCO), 2013.	
	A. Iscen, A. Agogino, V. SunSpiral, and K. Tumer. Learning to control complex tensegrity robots. In Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems, AAMAS ’13, pages 1193–1194, Richland, SC, 2013. International Foundation for Autonomous Agents and Multiagent Systems.	
Prize Breakdown among coauthors	Atil Iscen 100%	

Abstracts	
Controlling Tensegrity Robots through Evolution
Tensegrity structures (built from interconnected rods and cables) have the potential to offer a revolutionary new robotic design that is light-weight, energy-efficient, robust to failures, capable of unique modes of locomotion, impact tolerant, and compliant (reducing damage between the robot and its environment). Unfortunately robots built from tensegrity structures are difficult to control with traditional methods due to their oscillatory nature, nonlinear coupling between components and overall complexity. Fortunately this formidable control challenge can be overcome through the use of evolutionary algorithms. In this paper we show that evolutionary algorithms can be used to efficiently control a ball shaped tensegrity robot. Experimental results performed with a variety of evolutionary algorithms in a detailed soft-body physics simulator show that a centralized evolutionary algorithm performs 400% better than a hand-coded solution, while the multiagent evolution performs 800% better. In addition, evolution is able to discover diverse control solutions (both crawling and rolling) that are robust against structural failures and can be adapted to a wide range of energy and actuation constraints. These successful controls will form the basis for building high-performance tensegrity robots in the near future. 
Learning to Control Complex Tensegrity Robots
Tensegrity robots are based on the idea of tensegrity structures that provides many advantages critical to robotics such as being lightweight and impact tolerant. Unfortunately tensegrity robots are hard to control due to overall complexity. We use multiagent learning to learn controls of a ball-shaped tensegrity with 6 rods and 24 cables. Our simulation results show that multiagent learning can be used to learn an efficient rolling behavior and test its robustness to actuation noise.	

Statement about “why judges should consider the entry as “best”	
Tensegrity structures are composed of compressional elements such as rigid bodies that are connected through tensional elements such as cables.  Despite the fact that the rigid bodies are not directly connected, the structure have an internal balance via the network of tensions. Tensegrity structures bring many advantages and a different approach to the robotics locomotion:
	•	-Lightweight and simple: Since tensional elements hold the structure, they are lightweight compared to typical robots.
	•	-Flexible and can handle collisions: Due to flexibility of the tensional elements, the structure can handle collisions by  distributing external forces to all structure.
	•	-Different modes of locomotion such as crawling, hopping or rolling. 
	•	-Robust to failures: Since different rigid bodies are connected through a network of tensional elements, they can handle failures of single elements.
	•	-Robust to noise:  The structure can handle activation noise for rolling motion.
	•	-Distributed Controls:  Each part of the structure can be controlled by different controllers. This allows usage of distributed learning algorithms and increases robustness. 
Despite these advantages,  active controls of tensegrity robots is nearly impossible with classical control methods and human engineer-centric control design. First, this has a structure and a form of oscillatory locomotion that is completely unusual from human perspective.  Humans are not experienced with actively controlling a tensegrity structure. Every movement effects the whole structure and as a whole the movements are oscillatory.  Moreover locomotion using rolling behavior shown to be efficient in our work is not heavily studied. From theoretical perspective, the classical control methods have hard time solving the controls problem even for shapeshifting. The network of tensions bring all nonlinear equations connected to each other (24 equations in the simplest sphere shape tensegrity). One step further than shapeshifting,  locomotion brings additional challenges such as finding right consecutive deformations to provide locomotion. Furthermore, locomotion by rolling behavior is an even more complicated problem since we do not know the right shape deformations that will provide rolling.  These are the reasons behind the failure of hand coded algorithms and the reason we used coevolutionary algorithms to reveal the power of tensegrity robots.
Considering the challenges mentioned above, the literature contains few studies about controlling tensegrity robots.  In our papers we refer to these works. One approach used evolutionary control of a 3 strut crawling tensegrity. While this was a great first step in enabling any control at all, such a simple structure and resulting awkward slow movement could not begin to show the full potential of tensegrity robotics. We also cite a work about 6 strut rolling motion, that concentrates on one full static rotation, with slow deformations. Instead, our research concentrates on smooth and repeating rolling motion.	
Considering the results in our papers (AAMAS 12, GECCO 12), we develop a successful control policy using sinusoidal signals and coevolutionary algorithms. The control policy that we provide is distributed in the sense that each part of the robot controlled using an independent controller.  In our papers, we also show that the policy that coevolutionary algorithms provide is robust to failures (broken cable/string), noise (0-100% activation noise),  different actuation ranges. All these aspects of the provided policy are brought by distributed nature of the structure, distributed controls and flexibility provided by learning algorithms. 
The idea of providing successful locomotion for a tensegrity robot is definitely an important success for the robotics research. It brings a new perspective to the idea of robotics and robotics locomotion. In this respect, evolutionary algorithms proves a huge point:  To achieve a given behavior, using a structure that we are familiar with and improving its abilities might not be the best approach. Instead, one can use a structure that we humans, are not familiar with, and use evolutionary algorithms to reach the desired behavior. Our results are an example of this approach and how evolutionary algorithms can be integrated in this process.

Humies Competition 2013                                              Rolling Motion for a Tensegrity Robot