Evolution of Joint-Level Control for Quadrupedal Locomotion
We investigate a hierarchical approach to robot control inspired by joint-level control in animals. The method combines a high-level controller, consisting of an artificial neural network (ANN), with joint-level controllers based on digital muscles. In the digital muscle model (DMM), morphological and control aspects of joints evolve concurrently, emulating the musculoskeletal system of natural organisms. We introduce and compare different approaches for connecting outputs of the ANN to DMM-based joints. We also compare the performance of evolved animats with ANN-DMM controllers with those governed by only high-level (ANN-only) and low-level (DMM-only) controllers. These results show that DMM-based systems outperform their ANN-only counterparts while also exhibiting less complex ANNs in terms of the number of connections and neurons. The main contribution of this work is to explore the evolution of artificial systems where, as in natural organisms, some aspects of control are realized at the joint level
Evolutionary robotics [5, 11, 20, 32, 40, 49] borrows concepts from natural evolution and applies them to the design of simulated or physical robots. Many studies in this area focus on optimizing controllers for robots with fixed morphologies [12, 13]. Controllers such as ANNs  and central pattern generators (CPGs)  are amenable to evolutionary optimization and have produced various forms of robotic locomotion, including salamander gaits , bipedal walking , and crawling . Typically, these controllers generate outputs governing the movement of each joint, such as the desired angles of actuators, in addition to handling high-level decision making. In natural organisms, however, intrinsic properties of muscles themselves contribute to both stability and function . For example, the tendon network of the human hand has been shown to perform active tension modulation, independent of the neural system . Such observations have led researchers to explore the concurrent evolution of morphology and control [7, 10, 14, 33, 35, 43, 44, 47, 56]. Paul and Bongard  found that even small changes to a robotʼs mass distribution have large effects on resultant gaits, leading to unique control-morphology pairs in evolved individuals. Later, Bongard  demonstrated the importance of morphology in the evolutionary process as a contributor to robust behavior in an individual. Further work by Cheney et al.  has demonstrated the emergence of coupling in soft robots, whose gaits are dependent upon body shape and size. Other researchers have explored the offloading of control to morphological elements [6, 21, 41, 44]. For example, Rieffel et al.  demonstrated distributed morphological control in a tensegrity
The main contribution of this work is to report results on the evolution of artificial systems where, as in natural organisms, some aspects of control are realized at the joint level. The results of evolutionary experiments demonstrate that, when combined with high-level ANNs, ANN-DMM controllers outperform their ANN-only counterparts while also exhibiting less structural complexity in the ANN component. Furthermore, the phased searching experiments demonstrate that the ANN complexity can be further reduced with only a slight decrease in animat performance. Collectively, these results indicate that for ANN-DMM controllers, the low-level aspects of control can be compartmentalized or offloaded from the ANN, potentially freeing it to focus on other tasks.
We have investigated a model of joint-level control inspired by that of biological organisms yet applicable to the control of robotic systems. Prior experiments showed that evolved DMM-based systems exhibit effective gaits in a quadrupedal animat, even when driven by a simple periodic oscillating signal acting as a high-level controller. However, in such a system the movements are essentially hardwired. Our primary focus in this article is evolving DMM-based joints concurrently with a high-level ANN controller, enabling the system to respond dynamically to sensed information The resulting hybrid ANN-DMM controllers (and even the DMM-only controllers) consistently outperform their ANN-only counterparts in terms of distance traveled. The two connection strategies evaluated, singly connected and individually connected, exhibit similar fitnesses, but the former produces less complex ANNs in both number of neurons and number of connections. In all cases, however, the evolved networks are quite large, with tens of neurons and hundreds of connections. Phased searching drastically reduces network complexity with only a small reduction in performance. Given these results, it appears the DMM compensates for a less complex high-level controller by governing basic movements, at least on flat terrain. A possible implication is that a high-level controller is free to focus on other matters, including environmental dynamics that affect low-level control, as well as more implementation of complex behaviors. In future work, we plan to investigate the evolution of hybrid controllers in varied terrain and dynamic environments, which might engage the sensory capabilities of an ANN more than simple locomotion tasks.
- Alexander, R., & Vernon, A. (1975). The mechanics of hopping by kangaroos (Macropodidae). Journal of Zoology, 177(2), 265–303.
- Autumn, K., Sitti, M., Liang, Y. A., Peattie, A. M., Hansen, W. R., Sponberg, S., Kenny, T. W., Fearing, R., Israelachvili, J. N., & Full, R. J. (2002). Evidence for van der Waals adhesion in gecko setae. Proceedings of the National Academy of Sciences of the U.S.A., 99(19), 12252–12256.
- Bongard, J. (2011). Morphological change in machines accelerates the evolution of robust behavior. Proceedings of the National Academy of Sciences of the U.S.A., 108(4), 1234–1239.
- Brooks, R. A. (1986). A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation, 2(1), 14–23.
- Brooks, R. A. (1989). A robot that walks; emergent behaviors from a carefully evolved network. Neural Computation, 1(2), 253–262.
- Bruce, J., Caluwaerts, K., Iscen, A., Sabelhaus, A., & SunSpiral, V. (2014). Design and evolution of a modular tensegrity robot platform. In Proceedings of the 2014 IEEE International Conference on Robotics and Automation (pp. 3483–3489). New York: IEEE Press.
- Chaumont, N., Egli, R., & Adami, C. (2007). Evolving virtual creatures and catapults. Artificial Life, 13(2), 139–157.
- Cheney, N., MacCurdy, R., Clune, J., & Lipson, H. (2013). Unshackling evolution: Evolving soft robots with multiple materials and a powerful generative encoding. In Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation (pp. 167–174). New York: ACM.
- Chervenski, P. (2016). MultiNEAT: Portable neuroevolution library. https://github.com/peter-ch/ MultiNEAT.
- Chiel, H. J., & Beer, R. D. (1997). The brain has a body: Adaptive behavior emerges from interactions of nervous system, body and environment. Trends in Neurosciences, 20(12), 553–557.