Brains on wheels: Mobile robots for brain research

Two mysteries of brains are focussed in this article from a roboticist's view. The rst is the brain's capacity to make sense of real-life sensoric input, which di ers strongly from experimentally administered stimuli. Another riddle posed by biological brains lies in the fact that brain subsystems are functionally indeterminate when seen in isolation, and acquire di erent functions when they interact with di erent other subsystems and receive di erent sensor input (\multi-functionality"). The paper describes insights gained in designing robots which directly relate to these two fundamental questions. Experiences with a \behavior-based" robot, the Black Knight (BK), are reported in some detail. BK had to work in an environment which was so messy that a formal modeling of the sensoric stimuli expected for the robot was out of the question. BK's control program makes do without explicit representations of external circumstances, and relies on \acting things out" instead. This leads to a deeper understanding of the real-life input problem. With respect to the multi-functionality problem, BK o ers surprising insights since it is construed and programmed as a dynamical system comprised of many open subsystems (\behaviors"). Open dynamical systems intrinsically are functionally indeterminate. While the reported methodology and results are of some interest for their own sake, the superordinate goal of this paper is to promote robot experiments as a tool for brain research. 1 Two things we don't understand about brains In this article I want, (i) to put my ngers on what I perceive to be two blind spots in current biocybernetic models of vertebrate brain systems (e.g., Yao & Freeman 1990, Ewert et al 1994, Corbacho & Arbib 1995, Grossberg 1995), and (ii) to vote for robot experiments as a way to sharpen our eyesight. In order to add empirical substance to methodological argument, the main body of this contribution reports on experiences with a little robot, the \Black Knight". The two blind spots I want to address are (i) the nature of real-life sensoric input, and how the brain makes sense of it, and (ii) the fact that a given neural structure can participate in many functions. I will now explain these two points in more detail. Real-life sensoric input: The input with which biocybernetic models are tested in simulations or physical experiments is typically highly standardized. Examples are regular bar patterns; \smells" encoded as constant activation levels of simulated receptor cells (Yao & Freeman 1990); prey worms positioned on a laboratory surface behind a barrier of vertical bars in standardized distances (Corbacho & Arbib 1995). Such stimuli, simulated or physical, have in common that they can be succinctly described by the experimentator. That stimuli can be described is an obvious precondition for repeatability of experiments (and for writing papers). However, it is doubtful whether real-life input to physical brain subsystems is describable with so many words. Real-life sensor input is high-dimensional, varies on many timescales, has very strong stochastic components; in almost every formal aspect, it is ill-behaved. For instance, a toad approaching a worm in the wild will be confronted with a \pattern" which is, according to circumstances, glistening or dim, darker or lighter as the background, vanishing or appearing, twitching or immobile, big or small, brownish or blackish or reddish or brightly yellow, segmented or smooth, etc., etc.; and to make things worse, the \background" su ers from the same untamed variability. Furthermore, many of these aspects may or may not change on any odd timescale. The very notion of a pattern is, in fact, challenged by this variability. Of course, invariants of some sort must exist (otherwise the toad would not be able to catch a worm now and then). But I don't think that the challenge of modelling such \undescribable" invariants has yet been acknowledged by many (a rst step is Bingham's (1995) investigation of qualitative di erences between dynamical light point patterns). Multiple functions: Biological neural systems typically serve many di erent functions. Their functional exibility ba es investigators in that for nearly every functional mechanism hypothesized for a given neural system, data can be collected which conrm that hypothesis. A striking example is the cerebellum. It seems to be a general hypothesis-veri cation device. For almost every motor control mechanism devised by control theorists, empirical recordings can be obtained which demonstrate that the cerebellum indeed implements this mechanism (compare e.g., Bloedel 1992, Miall 1993, Kawato & Gomi 1992). This multi-functionality of real brain subsystems forms a striking contrast to biocybernetic models. They are typically made of modules which serve a single purpose each.1 A related observation is that biological brains are quite robust in the face of (at least some) parameter changes. A drugged brain will change the way it functions, even quite drastically, but it often will still function in a way that makes some sense, if not the sense it made before. For an illustration, consider how human behavior changes when subjects are drunk. By contrast, biocybernetic models are often highly sensitive with respect to the setting of parameters. Typically, the (single) functionality of such a system breaks down when the model's parameters leave a narrow margin, { and this functionality is not replaced by another. In this situation, where epistemological consequences are as intransparent as the situation itself, it is interesting to see how roboticists manage. They have somehow to make their robots cope with the messiness of real-life sensor input. Furthermore, as will be explained in this article, some modern strategies of robot design also inherently run into the problem of multi-functionality. The article is organized in the following way. The next section provides an introduction to \behavior-based" robotics, a relatively young approach to building mobile robots which I believe is particularly relevant for brain research. In section 3 I describe a robot and its control program, which is designed in the behavior-based tradition, using di erential equations as a speci cation language. The main portion of this section, and of the entire article, is a detailed discussion of what can be learnt from this robot with respect to the questions of real-life sensoric input and multiple functions. In the nal discussion (section 4) the arguments from preceding sections are revised and bundled. 2 Behavior-based robotics Many strands in academic research and industrial development are called \robotics". In this article, I refer to a particular, relatively young tradition variously named \behaviororiented [or behavior-based] robotics", \bottom-up robotics", or \New AI". Behaviororiented robotics has arisen in a multi-disciplinary context between classical robotic manipulator engineering, arti cial intelligence, cognitive science, ethology, biology, and \arti cial life" research. This particular kind of robotics has a strong avor of basic research, although recently decisive steps toward economically relevant applications have been taken (e.g., the sewage inspection robots developed in the group I belong to, cf. Kirchner 1996). An ancestor of behavior-based robotics is Braitenberg's (1984) famous booklet on \vehicles"; good contemporary introductions are Brooks (1991) and Steels (1993); a comprehensive textbook has recently been written by Pfeifer and Scheier (1996). The goal of research in this eld is generally to build and understand (in this sequence) mobile \agents" that can swiftly and robustly cope with the messiness of physical environments. Naive observers are often amazed how strongly behavior-based robots remind them of live insects: they are typically quite small, agile, fast (compared to their size and other kinds of robots, that is...), and almost incessantly active. Cooperations with ethologists and biologists are common, especially with insect researchers (Deneubourg et al 1991, Cruse et al 1995). The eld is tied together by several methodological commitments (thoroughly explained in Pfeifer & Scheier 1996). Out of these principles, I pick three which are particularly relevant for connecting behavior-oriented robotics with brain research: 2 Integrated agents: In the perspective of behavior-oriented robotics, it does not make much sense to investigate single information processing faculties like \vision", \knowledge representation", or \motor control". It is believed that every \mechanism" in an agent is intimately related to almost every other, and even more, that every mechanism is related to the body's hardware; furthermore, it is stated that an embodied intelligent information processing system can only be understood (and designed) when the agent's overt motor behavior is integrated into the picture. To push the integrative view even further, it is also claimed that the agent's goals and motivations must be included into the picture, and, of course, the characteristics of the environment the agent is adapted to. Thomas Christaller calls this the \oyster principle" (personal communication): one has to swallow the thing wholesale, or not at all. The emphasis on integratedness has two immediate consequences. First, each of the agent's \behaviors" (like walking, recharging, picking an object) is conceived as an integrated entity that covers the complete internal pathway from sensing to motor command generation, including (possibly) specialized representations and motivational mechanisms. This di ers from the classical arti cial intelligence (AI) approach to robotics, where sensor stimuli are rst processed into a general representational format which is then globally available for further use. Second, a behavior is not considered in isolation. The agent is viewed as a system of many interacting behaviors, indeed, as a s