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Design Principles for Biologically Inspired Cognitive Robotics Author's personal copy Biologically Inspired Cognitive Architectures (2012) 1,73– 81 Available at www.sciencedirect.com journal homepage: www.elsevier.com/locate/bica INVITED ARTICLE Design principles for biologically inspired cognitive robotics Jeffrey L. Krichmar Department of Cognitive Sciences, 2328 Social and Behavioral Sciences Gateway, University of California, Irvine 92697-5100, USA Department of Computer Science, 2328 Social and Behavioral Sciences Gateway, University of California, Irvine 92697-5100, USA Received 5 April 2012; received in revised form 28 April 2012; accepted 28 April 2012 KEYWORDS Abstract Cognition; The goals of cognitive robotics are to better understand cognition through the construction of Computational physical artifacts, and to create practical systems that demonstrate cognitive capabilities. I neuroscience; believe for cognitive robotics to move forward, a balanced approach that emphasizes the inter- Embodiment; Neurorobots action of brain, body, and environment is necessary. In general, cognitive robots and cognitive architectures focus too much on brain control, and overlook the contributions of morphology to intelligent behavior. On the other hand, the behavior based robotics approach is unbalanced in the opposite direction. For cognitive robotics to move forward, these disparate research com- munities need to come into balance. The materials, morphology, sensors, actuators, and the nervous system should be balanced and coordinated in their action. In their book, ‘‘How the body shapes the way we think: A new view of intelligence’’ (MIT Press, 2007), Pfeifer and Bongard have suggested that intelligent agents should follow a set of design principles that high- light the importance of embodiment and physical interaction with the environment. In the pres- ent paper, I apply each of these principles to biologically inspired cognitive robotics and suggest how the field can shift toward better cognitive architectures by adherence to these principles. ª 2012 Elsevier B.V. All rights reserved. Introduction In the field of cognitive robotics, physical artifacts are con- structed to demonstrate a level of cognition. The term cog- nitive is difficult to define because it has different meaning Address: Department of Cognitive Sciences, 2328 Social & to different people. In this paper, I will focus on comparing Behavioral Sciences Gateway, University of California, Irvine, CA 92697-5100, USA. Tel.: +1 949 824 5888. agent behavior to that of biological organisms carrying out E-mail address: [email protected] cognitive tasks, such as standard tasks of attention, 2212-683X/$ - see front matter ª 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.bica.2012.04.003 Author's personal copy 74 J.L. Krichmar decision-making, learning and memory. This allows artificial I suggest that we in the Biologically Inspired Cognitive Archi- agents to be compared to a working model. Therefore, tectures (BICA) community should embrace many of these throughout the paper the term ‘‘cognition’’ could be re- design principles when developing our systems. I will apolo- placed by ‘‘biologically inspired cognition’’. Also, through- gize in advance for not referencing many pertinent robots out the paper I will use the term ‘‘nervous system’’ to and systems in the present paper. In general, I will focus describe the system that guides the agent behavior. on systems that I know at a deep level because either I have Although many of the examples given are based on neuro- had a hand in their design or because I have seen a particular biologically inspired systems, this does not mean to imply robot in person and discussed the robot with its designers. that a neurobiologically inspired architecture is the only method for constructing a cognitive robot. But, the neuro- biologically inspired approach does allow the mechanism Design principles for cognitive robots to be compared with empirical data. Despite these restric- tions, the arguments below may be applied to cognitive Pfeifer and Bongard introduced eight design principles for robotics in general. intelligent agents (Pfeifer & Bongard, 2007). I will follow The field of cognitive robots goes by many names: brain- each of these in turn, and discuss them in light of biologi- based devices, cognitive robots, neurorobots, neuromorphic cally inspired cognitive robot design. robots, etc. The common goal is twofold: Firstly, developing a system that demonstrates some level of cognitive ability Agent design principle 1: the three-constituents can lead to a better understanding of cognition in general. principle This idea has been dubbed ‘‘synthetic methodology’’ or ‘‘synthetic neural modeling’’ and the notion goes back to An intelligent agent should have (1) a defined ecological Grey Walter’s Turtles, Braitenberg’s vehicles and the Darwin niche, (2) a defined behavior or task, and (3) an agent series of automata (Braitenberg, 1986; Edelman et al., 1992; design. Grey Walter, 1953). The synthetic method has even older The first two constituents define the agent’s behavioral roots in psychology and cybernetics (Cordeschi, 2002; Craik, task. For example, the niche and behavior of many of our 1967). In general, understanding through building is the goal. robots has been a controlled laboratory setting. However, Secondly, building a robot or artifact that follows a cognitive much of cognitive science and behavioral neuroscience is model could lead to a system that demonstrates capabilities conducted in experiments where humans and other animals commonly found in the animal kingdom, but rarely found in behave under controlled settings in darkened rooms. There- artificial systems. While this second goal may have important fore, cognitive robots, which are built to test theories of implications for practical applications, cognitive robotics biological cognition, are often tested in conditions that mi- have not been as successful in achieving this goal as origi- mic these experimental paradigms. For example, cognitive nally hoped. One of the aims of this paper is to discuss some robots have replicated standard experimental paradigms of the reasons why this is the case. such as operant conditioning, fear conditioning, and skill In their book, ‘‘How the body shapes the way we think: A acquisition to better understand learning and memory new view of intelligence’’, Pfeifer and Bongard put forth an (Krichmar & Edelman, 2002; McKinstry, Seth, Edelman, & embodied approach to cognition (Pfeifer & Bongard, 2007). Krichmar, 2008). We have tested our robots in the Morris Because of this position, many of the robots that they have water maze or the Plus maze to understand how different designed demonstrate ‘‘intelligent’’ behavior with limited neural areas contribute to different types of memory (Flei- or non-existent neural processing (Bongard, Zykov, & Lip- scher, Gally, Edelman, & Krichmar, 2007; Krichmar, Nitz, son, 2006; Iida & Pfeifer, 2004). Gally, & Edelman, 2005). In many ways, the field of cognitive robotics and my own The main reason for this is to compare the cognitive ro- work on brain-based devices and neurorobotics might be re- bot’s behavior with biological cognition. This can’t be garded as the antithesis of Pfiefer and Bongard’s position. stressed enough. If we are to claim that our robots are cogni- Our designs are heavy on top-down control and neural pro- tive, then we need to test them under conditions by which cessing and light on interaction with the environment. I will cognitive scientists test their subjects. However, the flip side discuss why this is the case in detail below. Although they of this is that it does not make for exciting robot behavior and may underemphasize them, cognitive robots do adhere to it can potentially reveal limitations in the cognitive robot ap- many of Pfeifer and Bongard’s principles. It is my belief that proach. The community needs to demonstrate that cognitive cognitive robots and architectures should attempt to follow robots can transition to the real world just as humans or ani- these principles. mals do when they are outside a laboratory setting. In the remainder of the paper, I will discuss how each of Some roboticists have been able to demonstrate that Pfeifer and Bongard’s principles for designing intelligent their neuromorphic systems are effective outside of labora- agents can apply to brain-based or neuromorphic robots. tory settings. For example, the RatSLAM project has devel- Many readers may not agree Pfeifer and Bongard’s point of oped accurate cognitive maps of offices and cities based on view. For example, Clark and Grush take the position that a hippocampal inspired architecture (Wyeth & Milford, cognitive phenomena involves offline-reasoning, vicarious 2009), and our group has shown that a brain-based device environmental exploration and an internal representation can compete on the soccer field (Fleischer et al., 2006). (Clark & Grush, 1999). However, I believe there is value in The third constituent, agent design, is dealt with in examining these principles, which arise from behavior based cognitive or neuromorphic robots by necessity. If one is robotics (Arkin, 1998; Brooks, 1991), and seeing how they testing visual object recognition, then the robot will typi- might be implied to biologically inspired cognitive robotics. cally have a vision system, or if one is testing somatosensory Author's personal copy Design principles for biologically inspired cognitive robotics 75 tem that responded to features in its environment, but it also
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