1454 Part G Human-Centered and Life-Like Robotics allel adaptive computation that has seen application in inspired robot behaviors.and then showing how differ- robot vision systems and controllers but here we empha- ent additional mechanisms yield a variety of enriched size neural networks derived from the study of specific behaviors.Braitenberg's book [62.2]is very much in neurobiological systems.Neurorobotics has a twofold this spirit and has entered the canon of neurorobotics. aim:creating better machines which employ the prin- While their work provides a historical background for ciples of natural neural computation;and using the study the studies surveyed here,we instead emphasize stud- of bio-inspired robots to improve understanding of the ies inspired by the computational neuroscience of the functioning of the brain.Chapter 60,Biologically In-mechanisms serving vision and action in the human and spired Robots,complements our study of brain design animal brain.We seek lessons from linking behavior to with work on body design,the design of robotic con-the analysis of the internal workings of the brain (1)at trol and actuator systems based on careful study of the the relatively high level of characterizing the functional relevant biology. roles of specific brain regions (or the functional units Walter [62.1]described two biologically inspired of analysis called schemas Sect.62.2.4),and the behav- robots,the electromechanical tortoises Machina spec-iors which emerge from the interactions between them ulatrix and M.docilis (though each body has wheels and (2)at the more detailed level of models of neu- not legs).M.speculatrix has a steerable photoelectric ral circuitry linked to the data of neuroanatomy and cell,which makes it sensitive to light,and an electri-neurophysiology.There are lessons for neurorobotics cal contact,which allows it to respond when it bumps to be learned from even finer-scale analysis of the bio- into obstacles.The photoreceptor rotates until a light of physics of individual neurons and the neurochemistry moderate intensity is registered,at which time the or-of synaptic plasticity but these are beyond the scope ganism orients itself towards the light and approaches of this chapter(see Segev and London [62.3]and Freg- it.However,very bright lights,material obstacles,and nac [62.4].respectively,for entry points into the relevant steep gradients are repellent to the tortoise.The lat- computational neuroscience). ter stimuli convert the photoamplifier into an oscillator, The plan of this Chapter is as follows.After some which causes alternating movements of butting and selected examples from computational neuroethology, withdrawal,so that the robot pushes small objects out of the computational analysis of neural mechanisms under- its way,goes around heavy ones,and avoids slopes.The lying animal behavior,we show how perceptual and tortoise has a hutch.which contains a bright light.When motor schemas and visual attention provide the frame- the machine's batteries are charged,this bright light is work for our action-oriented view of perception,and repellent.When the batteries are low,the light becomes show the relevance of the computational neuroscience attractive to the machine and the light continues to ex- to robotic implementations (Sect.62.2).We then pay ert an attraction until the tortoise enters the hutch,where particular attention to two systems of the mammalian the machine's circuitry is temporarily turned off until the brain,the cerebellum and its role in tuning and coordi- batteries are recharged,at which time the bright hutch nating actions (Sect.62.3),and the mirror system and light again exerts a negative tropism.The second robot,its roles in action recognition and imitation(Sect.62.4). M.docilis was produced by grafting onto M.speculatrix The extroduction will then invite readers to explore the a circuit designed to form conditioned reflexes.In one many other areas in which neurorobotics offers lessons experiment,Walter connected this circuit to the obstacle- from neuroscience to the development of novel robot Part avoiding device in M.speculatrix.Training consisted of designs.What follows.then.can be seen as a contribu- blowing a whistle just before bumping the shell. tion to the continuing dialogue between robot behavior ⊙ Although Walter's controllers are simple and not and animal and human behavior in which particular 62.2 based on neural analysis,they do illustrate an attempt emphasis is placed on the search for the neural under- to gain inspiration from seeking the simplest mecha- pinnings of vision,visually guided action,and cerebellar nisms that will yield an interesting class of biologically control. 62.2 Neuroethological Inspiration Biological evolution has yielded a staggering variety of cific niches.One may thus turn to the neuroethology of creatures,each with brains and bodies adapted to spe- specific creatures to gain inspiration for special-purpose1454 Part G Human-Centered and Life-Like Robotics allel adaptive computation that has seen application in robot vision systems and controllers but here we empha￾size neural networks derived from the study of specific neurobiological systems. Neurorobotics has a twofold aim: creating better machines which employ the prin￾ciples of natural neural computation; and using the study of bio-inspired robots to improve understanding of the functioning of the brain. Chapter 60, Biologically In￾spired Robots, complements our study of brain design with work on body design, the design of robotic con￾trol and actuator systems based on careful study of the relevant biology. Walter [62.1] described two biologically inspired robots, the electromechanical tortoises Machina spec￾ulatrix and M. docilis (though each body has wheels not legs). M. speculatrix has a steerable photoelectric cell, which makes it sensitive to light, and an electri￾cal contact, which allows it to respond when it bumps into obstacles. The photoreceptor rotates until a light of moderate intensity is registered, at which time the or￾ganism orients itself towards the light and approaches it. However, very bright lights, material obstacles, and steep gradients are repellent to the tortoise. The lat￾ter stimuli convert the photoamplifier into an oscillator, which causes alternating movements of butting and withdrawal, so that the robot pushes small objects out of its way, goes around heavy ones, and avoids slopes. The tortoise has a hutch, which contains a bright light. When the machine’s batteries are charged, this bright light is repellent. When the batteries are low, the light becomes attractive to the machine and the light continues to ex￾ert an attraction until the tortoise enters the hutch, where the machine’s circuitry is temporarily turned off until the batteries are recharged, at which time the bright hutch light again exerts a negative tropism. The second robot, M. docilis was produced by grafting onto M. speculatrix a circuit designed to form conditioned reflexes. In one experiment, Walter connected this circuit to the obstacle￾avoiding device in M. speculatrix. Training consisted of blowing a whistle just before bumping the shell. Although Walter’s controllers are simple and not based on neural analysis, they do illustrate an attempt to gain inspiration from seeking the simplest mecha￾nisms that will yield an interesting class of biologically inspired robot behaviors, and then showing how differ￾ent additional mechanisms yield a variety of enriched behaviors. Braitenberg’s book [62.2] is very much in this spirit and has entered the canon of neurorobotics. While their work provides a historical background for the studies surveyed here, we instead emphasize stud￾ies inspired by the computational neuroscience of the mechanisms serving vision and action in the human and animal brain. We seek lessons from linking behavior to the analysis of the internal workings of the brain (1) at the relatively high level of characterizing the functional roles of specific brain regions (or the functional units of analysis called schemas Sect. 62.2.4), and the behav￾iors which emerge from the interactions between them, and (2) at the more detailed level of models of neu￾ral circuitry linked to the data of neuroanatomy and neurophysiology. There are lessons for neurorobotics to be learned from even finer-scale analysis of the bio￾physics of individual neurons and the neurochemistry of synaptic plasticity but these are beyond the scope of this chapter (see Segev and London [62.3] and Freg￾nac [62.4], respectively, for entry points into the relevant computational neuroscience). The plan of this Chapter is as follows. After some selected examples from computational neuroethology, the computational analysis of neural mechanisms under￾lying animal behavior, we show how perceptual and motor schemas and visual attention provide the frame￾work for our action-oriented view of perception, and show the relevance of the computational neuroscience to robotic implementations (Sect. 62.2). We then pay particular attention to two systems of the mammalian brain, the cerebellum and its role in tuning and coordi￾nating actions (Sect. 62.3), and the mirror system and its roles in action recognition and imitation (Sect. 62.4). The extroduction will then invite readers to explore the many other areas in which neurorobotics offers lessons from neuroscience to the development of novel robot designs. What follows, then, can be seen as a contribu￾tion to the continuing dialogue between robot behavior and animal and human behavior in which particular emphasis is placed on the search for the neural under￾pinnings of vision, visually guided action, and cerebellar control. 62.2 Neuroethological Inspiration Biological evolution has yielded a staggering variety of creatures, each with brains and bodies adapted to spe￾cific niches. One may thus turn to the neuroethology of specific creatures to gain inspiration for special-purpose Part G 62.2