1 00:00:01,640 --> 00:00:04,040 The following content is provided under a Creative 2 00:00:04,040 --> 00:00:05,580 Commons license. 3 00:00:05,580 --> 00:00:07,880 Your support will help MIT OpenCourseWare 4 00:00:07,880 --> 00:00:12,270 continue to offer high-quality educational resources for free. 5 00:00:12,270 --> 00:00:14,870 To make a donation or view additional materials 6 00:00:14,870 --> 00:00:18,830 from hundreds of MIT courses, visit MIT OpenCourseWare 7 00:00:18,830 --> 00:00:20,000 at ocw.mit.edu. 8 00:00:22,552 --> 00:00:24,010 TONY PRESCOTT: Great pleasure to be 9 00:00:24,010 --> 00:00:26,790 in Woods Hole, my first visit here, 10 00:00:26,790 --> 00:00:29,390 had a wonderful swim in the sea yesterday. 11 00:00:29,390 --> 00:00:33,790 Sheffield Robotics is across both universities in Sheffield. 12 00:00:33,790 --> 00:00:36,130 And it's been founded since 2011, 13 00:00:36,130 --> 00:00:39,490 but we've really been doing robotics since the 1980s. 14 00:00:39,490 --> 00:00:41,950 And I joined them in 1989. 15 00:00:41,950 --> 00:00:45,570 And we do pretty much every different kind of robotics, 16 00:00:45,570 --> 00:00:47,740 but I'm going to talk about biomimetics. 17 00:00:47,740 --> 00:00:50,650 I also do what you might call cognitive robotics. 18 00:00:50,650 --> 00:00:53,000 And I collaborate with Giorgio Metta on that, 19 00:00:53,000 --> 00:00:54,760 but since he's speaking too, I'm going 20 00:00:54,760 --> 00:00:57,430 to focus on the more animal-like robots 21 00:00:57,430 --> 00:00:59,680 that we've been developing. 22 00:00:59,680 --> 00:01:02,890 So this is one of our latest projects. 23 00:01:02,890 --> 00:01:08,260 This is a small, autonomous, mobile robot, 24 00:01:08,260 --> 00:01:10,330 which is a commercial platform, which 25 00:01:10,330 --> 00:01:13,840 will be available in the UK, I think, next January. 26 00:01:13,840 --> 00:01:16,570 And there will hopefully be a developer program 27 00:01:16,570 --> 00:01:19,330 for people that are interested in helping 28 00:01:19,330 --> 00:01:23,270 to develop the intelligence for this robot. 29 00:01:23,270 --> 00:01:27,310 So this is at a conference we had in Barcelona last month. 30 00:01:27,310 --> 00:01:31,000 And you can see that it's a robot pet. 31 00:01:31,000 --> 00:01:34,720 And we've been focusing on giving it 32 00:01:34,720 --> 00:01:38,650 some effective communication abilities, 33 00:01:38,650 --> 00:01:40,870 responding particularly to touch. 34 00:01:40,870 --> 00:01:44,990 You can see that it's orienting to stimuli. 35 00:01:44,990 --> 00:01:46,135 It has stereo vision. 36 00:01:46,135 --> 00:01:47,470 It has stereo sound. 37 00:01:47,470 --> 00:01:51,490 And it can orient to visual stimuli 38 00:01:51,490 --> 00:01:53,400 and also to auditory stimuli. 39 00:01:53,400 --> 00:01:55,640 Here we're showing it a picture of itself 40 00:01:55,640 --> 00:01:57,730 on this magazine cover. 41 00:01:57,730 --> 00:02:01,550 And the goal is to demonstrate that we can, 42 00:02:01,550 --> 00:02:05,320 in a commercial robot that will cost less than $1,000, 43 00:02:05,320 --> 00:02:08,770 considerably less, some of the principles of how 44 00:02:08,770 --> 00:02:12,140 the brain generates behavior. 45 00:02:12,140 --> 00:02:14,500 So this robot is called MiRo. 46 00:02:14,500 --> 00:02:19,360 It is based on some high-level principles abstracted 47 00:02:19,360 --> 00:02:22,450 from what we know about how mammalian brains control 48 00:02:22,450 --> 00:02:26,200 behavior, so it's a relatively complex robot, 13 49 00:02:26,200 --> 00:02:29,440 degrees of freedom, 3 arm processes corresponding 50 00:02:29,440 --> 00:02:31,360 to different levels, if you like, 51 00:02:31,360 --> 00:02:35,810 of the neuroaxis, the central nervous system. 52 00:02:35,810 --> 00:02:41,380 So once you start out with some general ideas, and questions, 53 00:02:41,380 --> 00:02:44,260 and issues about how we might learn 54 00:02:44,260 --> 00:02:47,890 from the biology in the brain, how to develop robots, 55 00:02:47,890 --> 00:02:51,730 and how we might use robots to help us understand the brain. 56 00:02:51,730 --> 00:02:54,880 And a central question I think that robotics 57 00:02:54,880 --> 00:02:58,170 can help us answer, and I think that's a core question 58 00:02:58,170 --> 00:03:01,510 in neuroscience, is what you might call the problem 59 00:03:01,510 --> 00:03:02,980 of behavioral integration. 60 00:03:02,980 --> 00:03:05,650 And the neuroscientist Ernest Barrington 61 00:03:05,650 --> 00:03:07,090 summarized this quite nicely. 62 00:03:07,090 --> 00:03:10,120 He said, "the phenomenon so characteristic of living 63 00:03:10,120 --> 00:03:13,660 organisms, so very difficult to analyze, the fact 64 00:03:13,660 --> 00:03:16,840 that they behave as wholes rather than as the sum 65 00:03:16,840 --> 00:03:18,700 of their constituent parts. 66 00:03:18,700 --> 00:03:21,090 Their behavior shows integration, 67 00:03:21,090 --> 00:03:23,820 a process unifying the actions of an organism 68 00:03:23,820 --> 00:03:27,140 into patterns that involve the whole individual." 69 00:03:27,140 --> 00:03:29,500 And this picture of a squirrel over here I think nicely 70 00:03:29,500 --> 00:03:30,260 demonstrates this. 71 00:03:30,260 --> 00:03:32,980 So of course this squirrel is leaping from one branch 72 00:03:32,980 --> 00:03:34,030 to the next. 73 00:03:34,030 --> 00:03:36,370 And you can see that every part of his body 74 00:03:36,370 --> 00:03:39,640 is coordinated and organized for this action, 75 00:03:39,640 --> 00:03:42,430 so you can see that his eyes looking straight ahead, 76 00:03:42,430 --> 00:03:44,830 his whiskers-- and I'll talk a lot more about whiskers-- 77 00:03:44,830 --> 00:03:46,250 pointing forward. 78 00:03:46,250 --> 00:03:49,930 His arms and his feet are all ready 79 00:03:49,930 --> 00:03:53,140 and they're there ready to catch his fall. 80 00:03:53,140 --> 00:03:55,840 Even his tail is angled and positioned 81 00:03:55,840 --> 00:03:57,670 to help him fly through the air. 82 00:03:57,670 --> 00:04:00,820 So it's the coordination of the different parts of the body, 83 00:04:00,820 --> 00:04:03,130 and the multiple degrees of freedom of the body, 84 00:04:03,130 --> 00:04:07,060 and the sensory systems in space and in time, which, I think, 85 00:04:07,060 --> 00:04:11,170 is a critical problem for biological control 86 00:04:11,170 --> 00:04:14,200 and also a problem for robots, which we're still struggling 87 00:04:14,200 --> 00:04:16,290 to address with our robots. 88 00:04:16,290 --> 00:04:19,209 And I want to give you two very general principles 89 00:04:19,209 --> 00:04:23,870 for thinking about how brains solve this problem. 90 00:04:23,870 --> 00:04:27,700 So many of you will have come across Rodney Brooks, 91 00:04:27,700 --> 00:04:29,710 yes, from MIT. 92 00:04:29,710 --> 00:04:32,020 And he's famous for, in robotics, 93 00:04:32,020 --> 00:04:36,340 the notion of layered control, which he called subsumption. 94 00:04:36,340 --> 00:04:39,370 And I think the ideas that he brought 95 00:04:39,370 --> 00:04:41,740 into robotics really changed how people thought 96 00:04:41,740 --> 00:04:44,020 about robots in the 1980s. 97 00:04:44,020 --> 00:04:49,060 But if we go back to the 1880s, John Hughlings Jackson, 98 00:04:49,060 --> 00:04:52,950 who was a British neurologist, proposed a similar idea, 99 00:04:52,950 --> 00:04:56,270 but with respect to the nervous system. 100 00:04:56,270 --> 00:05:01,030 So in the 1880s, people thought about the higher areas 101 00:05:01,030 --> 00:05:04,510 of the brain, particularly the cortex, 102 00:05:04,510 --> 00:05:08,710 as being about higher thought, and reasoning, and language, 103 00:05:08,710 --> 00:05:11,460 and not so much about perception and action. 104 00:05:11,460 --> 00:05:14,500 And Hughlings Jackson, I think, was revolutionary in his day 105 00:05:14,500 --> 00:05:17,770 in saying, that the highest motor senses represent over 106 00:05:17,770 --> 00:05:20,110 again in more complex combinations what 107 00:05:20,110 --> 00:05:22,072 the middle motor centers represent. 108 00:05:22,072 --> 00:05:24,530 In other words, he was saying that, the whole of the brain, 109 00:05:24,530 --> 00:05:27,470 all the way up, is about coordinating perception 110 00:05:27,470 --> 00:05:28,490 with action. 111 00:05:28,490 --> 00:05:31,460 And he described it in many senses as a layered system. 112 00:05:31,460 --> 00:05:33,080 He talked about how you could take off 113 00:05:33,080 --> 00:05:37,190 the top layers of the system and the competences of the lower 114 00:05:37,190 --> 00:05:39,440 layers remained intact, which, of course, is 115 00:05:39,440 --> 00:05:41,930 very much the idea of Rodney Brooks subsumption 116 00:05:41,930 --> 00:05:43,580 architecture. 117 00:05:43,580 --> 00:05:47,660 And some old studies, they did transection in animals 118 00:05:47,660 --> 00:05:49,805 like cats and rats-- demonstrate this nicely. 119 00:05:49,805 --> 00:05:53,120 So if you take a cat or a rat, particularly a rat, 120 00:05:53,120 --> 00:05:56,245 and you remove, in fact, all of the cerebral cortex, 121 00:05:56,245 --> 00:06:00,530 so if you make a slice here that takes away cortex, 122 00:06:00,530 --> 00:06:03,110 you get an animal that actually, to all appearances, 123 00:06:03,110 --> 00:06:04,280 looks fairly normal. 124 00:06:04,280 --> 00:06:06,660 It does motivated behavioral sequences, 125 00:06:06,660 --> 00:06:07,737 so it will get hungry. 126 00:06:07,737 --> 00:06:08,820 And it will look for food. 127 00:06:08,820 --> 00:06:10,220 And it will eat. 128 00:06:10,220 --> 00:06:12,200 If there's an appropriate mate nearby, 129 00:06:12,200 --> 00:06:16,220 it will look to have a sexual relationship. 130 00:06:16,220 --> 00:06:21,050 And it will fail in some challenges such as learning, 131 00:06:21,050 --> 00:06:25,000 and perhaps also in dexterous control, but in many ways, 132 00:06:25,000 --> 00:06:26,910 it will look normal. 133 00:06:26,910 --> 00:06:31,910 If you slice below the other part of the forebrain, 134 00:06:31,910 --> 00:06:34,610 the thalamus and the hypothalamus, 135 00:06:34,610 --> 00:06:37,100 you remove these areas, then you remove this capacity 136 00:06:37,100 --> 00:06:41,270 for motivated behavior, but you leave intact midbrain systems 137 00:06:41,270 --> 00:06:43,820 that can still generate individual actions. 138 00:06:43,820 --> 00:06:46,460 And if you remove parts of the midbrain, 139 00:06:46,460 --> 00:06:48,710 you leave intact, still, component movements, 140 00:06:48,710 --> 00:06:51,890 so for example, animals that can run on a treadmill. 141 00:06:51,890 --> 00:06:57,800 So we are, with our MiRo robot, loosely recapitulating 142 00:06:57,800 --> 00:07:01,070 this architecture, so we have three processors. 143 00:07:01,070 --> 00:07:03,720 And the idea with this robot, it's actually a part work, 144 00:07:03,720 --> 00:07:04,760 so you build it up. 145 00:07:04,760 --> 00:07:07,790 You get a magazine every week with a new part for the robot. 146 00:07:07,790 --> 00:07:10,580 And you build, essentially, a spinal robot first. 147 00:07:10,580 --> 00:07:12,620 And then you add a midbrain processor. 148 00:07:12,620 --> 00:07:15,500 Eventually you add a cortical processor, 149 00:07:15,500 --> 00:07:18,470 which gives with it some learning capacities, 150 00:07:18,470 --> 00:07:22,280 some pattern recognition, some navigation, so that's 151 00:07:22,280 --> 00:07:24,800 one principle, layered architecture, which 152 00:07:24,800 --> 00:07:29,360 seems to work both for biology, and perhaps in robotics. 153 00:07:29,360 --> 00:07:32,600 So second principle, and this goes back 154 00:07:32,600 --> 00:07:35,270 to another famous neuroscientist, 155 00:07:35,270 --> 00:07:39,050 this time Wilder Penfield, who is known to many people 156 00:07:39,050 --> 00:07:42,770 for his discovery of somatotopic maps in the brain, 157 00:07:42,770 --> 00:07:46,960 that if you stimulate, in the brain, in the area, 158 00:07:46,960 --> 00:07:49,940 the sensory area, then you find that people 159 00:07:49,940 --> 00:07:53,240 have experience of tickling on parts of the body. 160 00:07:53,240 --> 00:07:56,330 And adjacent parts of cortex correspond 161 00:07:56,330 --> 00:07:58,100 to adjacent parts of the body. 162 00:07:58,100 --> 00:08:00,830 And he found a similar homunculus in the motor area 163 00:08:00,830 --> 00:08:03,110 that you stimulate, and you get movement 164 00:08:03,110 --> 00:08:05,190 in adjacent parts of the body. 165 00:08:05,190 --> 00:08:08,650 And he also proposed another idea. 166 00:08:08,650 --> 00:08:13,040 And that was sort of a transencephalic dimension 167 00:08:13,040 --> 00:08:14,870 to nervous system organization. 168 00:08:14,870 --> 00:08:17,330 And that's to say that, down the midline 169 00:08:17,330 --> 00:08:19,330 of the central nervous system, there 170 00:08:19,330 --> 00:08:22,430 are a group of structures that don't seem to be specifically 171 00:08:22,430 --> 00:08:26,690 involved in specific aspects of perception and action, 172 00:08:26,690 --> 00:08:28,940 but seem to be about integration. 173 00:08:28,940 --> 00:08:32,010 And amongst them, particularly the basal ganglia, 174 00:08:32,010 --> 00:08:33,919 he noted as being important, and parts 175 00:08:33,919 --> 00:08:35,809 of the reticular formation. 176 00:08:35,809 --> 00:08:38,690 So Michael Frank was here talking to you 177 00:08:38,690 --> 00:08:41,330 about basal ganglia, so I'm not going 178 00:08:41,330 --> 00:08:45,350 to say much more about this, but this is just to point out, 179 00:08:45,350 --> 00:08:47,870 in an slice in the rat brain, these 180 00:08:47,870 --> 00:08:51,320 are the elements of the basal ganglia, particularly, 181 00:08:51,320 --> 00:08:53,720 the striatum is the input system. 182 00:08:53,720 --> 00:08:57,210 In the rat, the substantia nigra and part of the globus pallidus 183 00:08:57,210 --> 00:08:58,850 are the output systems. 184 00:08:58,850 --> 00:09:03,350 And then you have, in the rat brain, and also in our brains, 185 00:09:03,350 --> 00:09:05,720 you have massive convergence onto the input 186 00:09:05,720 --> 00:09:10,100 area, the caudate putamen it is also called, from the cortex 187 00:09:10,100 --> 00:09:11,610 and from the brain stem. 188 00:09:11,610 --> 00:09:14,000 So you have signals coming in from all over the brain 189 00:09:14,000 --> 00:09:16,490 to the striatum, which could be interpreted 190 00:09:16,490 --> 00:09:18,570 as a request for action. 191 00:09:18,570 --> 00:09:20,720 And then you have inhibition coming out 192 00:09:20,720 --> 00:09:23,030 from the output structures of the basal ganglia, 193 00:09:23,030 --> 00:09:25,370 here I'm showing it for the substantia nigra, 194 00:09:25,370 --> 00:09:28,040 going back to all of those areas of the brain. 195 00:09:28,040 --> 00:09:29,810 And this inhibition is tonic. 196 00:09:29,810 --> 00:09:31,760 And in order to have functional reaction, 197 00:09:31,760 --> 00:09:33,680 you have to remove the inhibition. 198 00:09:33,680 --> 00:09:39,770 So this is a system that can give you 199 00:09:39,770 --> 00:09:41,240 some of that behavioral integration 200 00:09:41,240 --> 00:09:43,790 that you need, the ability to ensure that you 201 00:09:43,790 --> 00:09:45,620 do one thing at a time. 202 00:09:45,620 --> 00:09:47,870 You do that quickly. 203 00:09:47,870 --> 00:09:50,150 You do that consistently. 204 00:09:50,150 --> 00:09:52,700 You dedicate all of your resources to the action 205 00:09:52,700 --> 00:09:54,870 that you want to do. 206 00:09:54,870 --> 00:09:57,200 Here's a little video of a rat. 207 00:09:57,200 --> 00:10:00,710 And I'm showing you some integrated behavior over time 208 00:10:00,710 --> 00:10:01,790 in an intact rat. 209 00:10:01,790 --> 00:10:05,600 So this is a rat exploring in a large container. 210 00:10:05,600 --> 00:10:08,089 And rats generally don't like open spaces, 211 00:10:08,089 --> 00:10:10,130 so when you first put the animal into this space, 212 00:10:10,130 --> 00:10:12,800 it will tend to stay near the walls. 213 00:10:12,800 --> 00:10:15,320 And it prefers this corner, which is dark. 214 00:10:15,320 --> 00:10:18,470 And of course, it's hungry too, so there's a dish of food here. 215 00:10:18,470 --> 00:10:20,120 And eventually, it gets up the courage 216 00:10:20,120 --> 00:10:21,710 to go out, collect a piece of food, 217 00:10:21,710 --> 00:10:26,060 and it will take it back into this dark corner to consume it. 218 00:10:26,060 --> 00:10:29,080 And one of the first models that we built 219 00:10:29,080 --> 00:10:32,200 was a model of basal ganglia operating as this, kind of, 220 00:10:32,200 --> 00:10:34,070 action-selection device. 221 00:10:34,070 --> 00:10:36,490 And with a simple Kepler robot, this 222 00:10:36,490 --> 00:10:39,870 is a robot that just really uses infrared sensors and a gripper 223 00:10:39,870 --> 00:10:40,900 arm. 224 00:10:40,900 --> 00:10:44,680 And we are using a model of the basal ganglia 225 00:10:44,680 --> 00:10:48,010 to control decision making about which actions 226 00:10:48,010 --> 00:10:51,820 to do at which time, and to generate sequencing 227 00:10:51,820 --> 00:10:53,150 of those actions. 228 00:10:53,150 --> 00:10:58,120 So as the need to stay close to walls diminishes, 229 00:10:58,120 --> 00:11:01,780 the robot, like the rat, goes and collects these cylinders. 230 00:11:01,780 --> 00:11:05,230 And it carries them back into the corners and deposits them. 231 00:11:05,230 --> 00:11:08,820 So a model of the central brain structures-- and I'm 232 00:11:08,820 --> 00:11:12,415 happy to discuss in more detail about how that model operates, 233 00:11:12,415 --> 00:11:14,560 and how similarities to the model 234 00:11:14,560 --> 00:11:16,270 that Michael will have described to you, 235 00:11:16,270 --> 00:11:19,000 but that's controlling the behavior switching, if you 236 00:11:19,000 --> 00:11:21,980 like, in this robot. 237 00:11:21,980 --> 00:11:27,250 So I spent some time working on this question of how 238 00:11:27,250 --> 00:11:30,850 central systems in the brain, particularly the basal ganglia, 239 00:11:30,850 --> 00:11:33,470 are involved in the integration of behavior, 240 00:11:33,470 --> 00:11:36,550 but I became frustrated with not understanding what 241 00:11:36,550 --> 00:11:39,740 were the signals coming in to the central brain structures 242 00:11:39,740 --> 00:11:42,820 and not understanding what effects those brain 243 00:11:42,820 --> 00:11:47,030 structures were having on the motor system of the animal. 244 00:11:47,030 --> 00:11:49,000 So I thought that what we needed to do 245 00:11:49,000 --> 00:11:51,190 was look at complete sensory motor loops. 246 00:11:51,190 --> 00:11:55,330 We needed to look at sensing and action, and how those interact. 247 00:11:55,330 --> 00:11:57,640 And in our Psychology Department, 248 00:11:57,640 --> 00:11:59,260 we have a neuroscience group that 249 00:11:59,260 --> 00:12:01,930 works mainly with rats, so it was natural for us 250 00:12:01,930 --> 00:12:03,670 to look at the rat. 251 00:12:03,670 --> 00:12:07,450 And in the rat, we know that one of the key perception systems 252 00:12:07,450 --> 00:12:09,200 is the vertebral system. 253 00:12:09,200 --> 00:12:11,560 So here you see, this is actually a pet rat, 254 00:12:11,560 --> 00:12:13,480 wandering around on my windowsill 255 00:12:13,480 --> 00:12:15,040 in my house in Sheffield. 256 00:12:15,040 --> 00:12:18,400 And the thing to notice is the whiskers here. 257 00:12:18,400 --> 00:12:20,500 And the whiskers are moving back and forth pretty 258 00:12:20,500 --> 00:12:24,430 much all the time that the rat is exploring. 259 00:12:24,430 --> 00:12:29,980 And we understand from nearly 100 years now of research 260 00:12:29,980 --> 00:12:32,530 that this system is very important for the rat 261 00:12:32,530 --> 00:12:33,860 to understand the environment. 262 00:12:33,860 --> 00:12:36,040 In fact, if it's completely dark, 263 00:12:36,040 --> 00:12:38,620 the rat would move around in much the same way. 264 00:12:38,620 --> 00:12:40,240 And it would be able to understand 265 00:12:40,240 --> 00:12:43,030 the world through touch pretty well, even 266 00:12:43,030 --> 00:12:45,970 in the absence of vision. 267 00:12:45,970 --> 00:12:48,310 So this is the same video, but now slow down 268 00:12:48,310 --> 00:12:51,910 10 times, and just to show you these movements 269 00:12:51,910 --> 00:12:54,400 of the whiskers, and how quite precise they are, 270 00:12:54,400 --> 00:12:58,540 because the rat isn't just, in a stereotypical way, 271 00:12:58,540 --> 00:13:00,640 banging its whiskers against the floor. 272 00:13:00,640 --> 00:13:02,530 It is lightly touching the whiskers 273 00:13:02,530 --> 00:13:04,690 in places it will get useful information. 274 00:13:04,690 --> 00:13:07,930 And you can see, when he puts his head over the window sill 275 00:13:07,930 --> 00:13:10,630 here, the whiskers push forward, as 276 00:13:10,630 --> 00:13:12,160 if he knows that he's going to have 277 00:13:12,160 --> 00:13:15,140 to reach further forward if he's going to find anything. 278 00:13:15,140 --> 00:13:17,590 Here you see him exploring this wooden cup. 279 00:13:17,590 --> 00:13:20,740 And you can see light touches by the whiskers. 280 00:13:20,740 --> 00:13:23,440 And you can also see that the movement of the whiskers 281 00:13:23,440 --> 00:13:26,050 is being modulated by the shape of the surface 282 00:13:26,050 --> 00:13:28,020 that he's investigating, so there's 283 00:13:28,020 --> 00:13:30,670 some fairly subtle control happening here. 284 00:13:30,670 --> 00:13:32,740 And I think it's not too much to say 285 00:13:32,740 --> 00:13:36,430 that the way in which the rat controls its whiskers 286 00:13:36,430 --> 00:13:38,200 has almost the same richness as the way 287 00:13:38,200 --> 00:13:40,030 that we control our fingertips. 288 00:13:42,560 --> 00:13:44,680 So I'm interested in how this plays out 289 00:13:44,680 --> 00:13:47,200 in terms of a layered architecture story. 290 00:13:47,200 --> 00:13:49,880 And of course, many people study this system. 291 00:13:49,880 --> 00:13:52,630 The beauty of it is, if you're a neuroscientist, 292 00:13:52,630 --> 00:13:55,040 that you can look in the cortex. 293 00:13:55,040 --> 00:13:57,310 This is rat cortex here. 294 00:13:57,310 --> 00:14:02,050 And a huge area of rat cortex is dedicated to somatosensation, 295 00:14:02,050 --> 00:14:06,010 to touch, of which a large area is dedicated to whiskers. 296 00:14:06,010 --> 00:14:07,480 In fact, you zoom in, you can find 297 00:14:07,480 --> 00:14:09,490 this area called barrel cortex. 298 00:14:09,490 --> 00:14:11,260 And with the right kind of staining, 299 00:14:11,260 --> 00:14:15,700 you can find groups of cells which preferentially receive 300 00:14:15,700 --> 00:14:18,070 signals from individual whiskers, 301 00:14:18,070 --> 00:14:21,430 so for example, you can move one whisker here, 302 00:14:21,430 --> 00:14:25,560 and you can know exactly where you record in the barrel cortex 303 00:14:25,560 --> 00:14:27,970 to get a very strong response from that whisker. 304 00:14:27,970 --> 00:14:30,820 And this means that barrel cortex and the whisker system 305 00:14:30,820 --> 00:14:35,170 has become one of the prepared, preferred preparations in which 306 00:14:35,170 --> 00:14:38,890 to study the cortical microcircuit altogether, 307 00:14:38,890 --> 00:14:41,650 so people study this system to really understand 308 00:14:41,650 --> 00:14:44,110 how cortex operates. 309 00:14:44,110 --> 00:14:46,390 Now, if we think about this system 310 00:14:46,390 --> 00:14:50,110 as a pathway from the whiskers up to barrel cortex, 311 00:14:50,110 --> 00:14:52,210 we're really only capturing one element 312 00:14:52,210 --> 00:14:54,940 of what's going on in the vertebral system. 313 00:14:54,940 --> 00:14:57,570 And that's this pathway here from the vertebrae, 314 00:14:57,570 --> 00:15:00,520 via the trigeminal complex, goes by the thalamus 315 00:15:00,520 --> 00:15:02,180 up to sensory cortex. 316 00:15:02,180 --> 00:15:05,920 And this is probably where 9 out of 10 papers on this system 317 00:15:05,920 --> 00:15:08,800 are published, but actually, this system 318 00:15:08,800 --> 00:15:11,420 is only part of a looped architecture, 319 00:15:11,420 --> 00:15:13,660 or we might say, a layered architecture. 320 00:15:13,660 --> 00:15:16,210 And at each level of this layered architecture, 321 00:15:16,210 --> 00:15:20,890 there's a completed loop, so that sensing can affect action, 322 00:15:20,890 --> 00:15:25,060 so sensing on the vertebrae can affect the movement and control 323 00:15:25,060 --> 00:15:26,770 of the vertebrae. 324 00:15:26,770 --> 00:15:28,810 So there's a loop via the brainstem 325 00:15:28,810 --> 00:15:32,470 here, so that, directly from the trigeminal complex, 326 00:15:32,470 --> 00:15:35,020 signals come back to the facial nucleus, which 327 00:15:35,020 --> 00:15:38,290 is where the motor neurons are that move the whiskers. 328 00:15:38,290 --> 00:15:40,600 There's a loop via the midbrain here 329 00:15:40,600 --> 00:15:43,390 so that sensory signals ascend very quickly 330 00:15:43,390 --> 00:15:45,490 to the midbrain superior colliculus. 331 00:15:45,490 --> 00:15:49,000 And they come back to affect how the whiskers move. 332 00:15:49,000 --> 00:15:51,970 And then, of course, there's the loop via the cortex 333 00:15:51,970 --> 00:15:54,750 too, so there's essentially those three loops, at least, 334 00:15:54,750 --> 00:15:56,920 that we need to think about. 335 00:15:56,920 --> 00:15:59,350 So since 2003, we've been building 336 00:15:59,350 --> 00:16:02,800 different whiskered robots, the aim being 337 00:16:02,800 --> 00:16:05,140 to instantiate our theories about how 338 00:16:05,140 --> 00:16:08,280 whiskered control works in this layered architecture 339 00:16:08,280 --> 00:16:11,080 and demonstrate it in a robot platform. 340 00:16:11,080 --> 00:16:13,540 And often, actually, building a robot platform 341 00:16:13,540 --> 00:16:15,760 causes us to ask new questions that might not 342 00:16:15,760 --> 00:16:19,210 be obvious to you just by doing biological experiments 343 00:16:19,210 --> 00:16:21,952 or even by doing simulation. 344 00:16:21,952 --> 00:16:23,410 Before I show you some robots, just 345 00:16:23,410 --> 00:16:28,120 quickly show a little bit more about the rat and its whiskers. 346 00:16:28,120 --> 00:16:30,230 So we began thinking we could just build robots, 347 00:16:30,230 --> 00:16:32,260 but we quickly realized that we didn't 348 00:16:32,260 --> 00:16:34,970 know enough about how rats use their whiskers to do that. 349 00:16:34,970 --> 00:16:37,874 And that's partly because the experiments that had been done 350 00:16:37,874 --> 00:16:39,790 haven't been done with the purpose of building 351 00:16:39,790 --> 00:16:41,260 a whiskered robot. 352 00:16:41,260 --> 00:16:43,810 So when you try and build a whiskered robot, 353 00:16:43,810 --> 00:16:46,680 you have to ask questions like, how do the whiskers move? 354 00:16:46,680 --> 00:16:49,600 And when you look at a video like this filmed from above, 355 00:16:49,600 --> 00:16:51,580 this is with a high speed camera, 356 00:16:51,580 --> 00:16:54,130 you think, well, the whiskers are sweeping backward 357 00:16:54,130 --> 00:16:56,110 and forward, like this. 358 00:16:56,110 --> 00:17:00,580 But in fact, if you put a mirror just tilted down here 359 00:17:00,580 --> 00:17:02,146 and you see what happens, then it 360 00:17:02,146 --> 00:17:03,770 turns out to be a little bit different, 361 00:17:03,770 --> 00:17:07,450 so you see that the whiskers are going up and down as much 362 00:17:07,450 --> 00:17:10,010 as they are going backwards and forwards. 363 00:17:10,010 --> 00:17:12,300 So the whiskers are actually sweeping like this, 364 00:17:12,300 --> 00:17:16,290 and they're making a series of touches on the surface. 365 00:17:16,290 --> 00:17:20,560 And if you watch, you can see that the whiskers are 366 00:17:20,560 --> 00:17:23,800 sort of playing down on the surface, sort of in a sequence, 367 00:17:23,800 --> 00:17:27,819 quite quickly, so that information might be giving you 368 00:17:27,819 --> 00:17:31,870 details about the shape of the surfaces in your world. 369 00:17:31,870 --> 00:17:33,640 So we mainly look at the long whiskers. 370 00:17:33,640 --> 00:17:35,590 This is a rat that's running up an alley. 371 00:17:35,590 --> 00:17:38,560 And we put an unexpected object in the alley, which 372 00:17:38,560 --> 00:17:41,950 could be this aluminum rectangle, 373 00:17:41,950 --> 00:17:44,350 or it could be this plastic step. 374 00:17:44,350 --> 00:17:47,050 And what you see is, if the animal encounters something 375 00:17:47,050 --> 00:17:49,420 unexpected with its long whiskers, then 376 00:17:49,420 --> 00:17:52,150 it turns very quickly and investigates it. 377 00:17:52,150 --> 00:17:54,970 So the long whiskers are like the periphery 378 00:17:54,970 --> 00:17:57,640 of a sensory system that has a fovea. 379 00:17:57,640 --> 00:18:00,070 And the fovea at the center of that system 380 00:18:00,070 --> 00:18:02,950 is a set of short whiskers around the mouth, also 381 00:18:02,950 --> 00:18:06,700 the lips and the nose, so that you 382 00:18:06,700 --> 00:18:09,670 can sniff and smell the surface that you're investigating. 383 00:18:09,670 --> 00:18:13,180 So we can zoom in and see that sensory fovea, here you 384 00:18:13,180 --> 00:18:16,690 see these short whiskers that are being used to investigate 385 00:18:16,690 --> 00:18:20,219 this plastic puck, and the longer whiskers investigating 386 00:18:20,219 --> 00:18:21,010 around the outside. 387 00:18:23,570 --> 00:18:26,710 So we have recapitulated elements 388 00:18:26,710 --> 00:18:28,890 of this layered architecture in our robot. 389 00:18:28,890 --> 00:18:31,060 And this is-- these are the loops. 390 00:18:31,060 --> 00:18:33,260 And this was about five years ago, 391 00:18:33,260 --> 00:18:35,650 we built a system with a brain stem loop, 392 00:18:35,650 --> 00:18:37,260 and really, midbrain loop. 393 00:18:37,260 --> 00:18:39,770 And this is our robot Scratchbot, 394 00:18:39,770 --> 00:18:43,750 which is the first of the whiskered robots 395 00:18:43,750 --> 00:18:47,395 that we felt really was capturing whisking in the way 396 00:18:47,395 --> 00:18:48,380 that the rat does it. 397 00:18:48,380 --> 00:18:52,780 It's running at about 4 hertz, whereas the real rat is 398 00:18:52,780 --> 00:18:55,150 whisking from 8 to 12 hertz, but it's 399 00:18:55,150 --> 00:18:58,780 scaled up to be about four times rat size. 400 00:18:58,780 --> 00:19:00,640 And what it's doing here is, it's 401 00:19:00,640 --> 00:19:02,680 using the whiskers to orient to stimuli, 402 00:19:02,680 --> 00:19:05,380 so this is Martin Pearson from Bristol Robotics Lab. 403 00:19:05,380 --> 00:19:08,290 He's putting an object in the whisker field. 404 00:19:08,290 --> 00:19:11,260 And the robot is turning and orienting 405 00:19:11,260 --> 00:19:12,980 to the touch with the object. 406 00:19:12,980 --> 00:19:15,520 It's putting its short micro vibrissae, in fact, 407 00:19:15,520 --> 00:19:18,020 against the object and exploring it. 408 00:19:18,020 --> 00:19:21,340 Now to do that, to detect a stimulus on the whiskers 409 00:19:21,340 --> 00:19:24,680 and then turn is not a fantastically hard task. 410 00:19:24,680 --> 00:19:27,640 The main challenge is to work out 411 00:19:27,640 --> 00:19:29,650 where the whisker was in its sweep 412 00:19:29,650 --> 00:19:31,270 when you made contact with an object, 413 00:19:31,270 --> 00:19:33,322 because the whisker is sweeping back and forth, 414 00:19:33,322 --> 00:19:34,780 so if you want to know the location 415 00:19:34,780 --> 00:19:36,370 of the point of contact, you need 416 00:19:36,370 --> 00:19:41,530 to integrate the position of the whisker in its sweep, what you 417 00:19:41,530 --> 00:19:44,590 might call a theta signal, and the presence 418 00:19:44,590 --> 00:19:46,210 of the contact on the whiskers. 419 00:19:46,210 --> 00:19:49,700 And the coincidence of those two is detected in the brain. 420 00:19:49,700 --> 00:19:52,210 And we know that there are cells in the barrel cortex 421 00:19:52,210 --> 00:19:54,520 that respond to that coincidence. 422 00:19:54,520 --> 00:19:57,340 So we have in our robot a model of the super colliculus, which 423 00:19:57,340 --> 00:19:59,200 is the location in the brain which we 424 00:19:59,200 --> 00:20:01,900 think is involved in orienting. 425 00:20:01,900 --> 00:20:04,480 And in our model of the colliculus, 426 00:20:04,480 --> 00:20:06,970 we have a head-centered map, which 427 00:20:06,970 --> 00:20:10,660 looks for this coincidence between a cell encoding 428 00:20:10,660 --> 00:20:13,370 the position of the whisker in its sweep and a cell 429 00:20:13,370 --> 00:20:17,710 encoding a contact and makes a turn to orient and explore 430 00:20:17,710 --> 00:20:20,177 that position. 431 00:20:20,177 --> 00:20:21,760 And then if we want to actually create 432 00:20:21,760 --> 00:20:24,860 behavior, which is integrated over time, 433 00:20:24,860 --> 00:20:28,030 so if the robot was just to orient every time it touched 434 00:20:28,030 --> 00:20:31,080 something, that wouldn't be very animal-like, 435 00:20:31,080 --> 00:20:32,960 particularly, you don't want to orient 436 00:20:32,960 --> 00:20:35,230 every time you touch the ground, so you just 437 00:20:35,230 --> 00:20:38,140 want to orient when you touch important stimuli. 438 00:20:38,140 --> 00:20:40,820 So we put into our model the basal ganglia 439 00:20:40,820 --> 00:20:44,020 so that we can decide whether the contact we've just made 440 00:20:44,020 --> 00:20:45,850 is something we want to investigate 441 00:20:45,850 --> 00:20:49,100 or something that doesn't interest us so much. 442 00:20:49,100 --> 00:20:52,570 So we have a system now with a midbrain 443 00:20:52,570 --> 00:20:55,630 that does orienting, a basal ganglia that makes decisions 444 00:20:55,630 --> 00:20:57,160 about sequencing. 445 00:20:57,160 --> 00:20:59,810 And those two things together give us 446 00:20:59,810 --> 00:21:03,450 reasonably lifelike behavior in our robot, Scratchbot. 447 00:21:03,450 --> 00:21:05,800 And that's quite a lot of what we 448 00:21:05,800 --> 00:21:08,710 have running now on the new robot, MiRo, is this system. 449 00:21:08,710 --> 00:21:11,180 It's for orienting and exploring. 450 00:21:11,180 --> 00:21:13,840 And we can use it-- we use it here for tactile orienting, 451 00:21:13,840 --> 00:21:15,520 but of course, the same system can 452 00:21:15,520 --> 00:21:17,170 underlie orienting to sounds, if you 453 00:21:17,170 --> 00:21:21,650 can localize those, and space, and orienting to visual stimuli 454 00:21:21,650 --> 00:21:23,020 too. 455 00:21:23,020 --> 00:21:25,990 So it turns out that this isn't a complete solution 456 00:21:25,990 --> 00:21:29,260 to the problem of orienting for our whiskered robot. 457 00:21:29,260 --> 00:21:31,900 And that's because sometimes our robot 458 00:21:31,900 --> 00:21:34,690 would stop as it was moving around 459 00:21:34,690 --> 00:21:38,024 and just move and investigate a point in space 460 00:21:38,024 --> 00:21:39,190 where nothing was happening. 461 00:21:39,190 --> 00:21:41,579 We call that a ghost orient. 462 00:21:41,579 --> 00:21:43,120 And the problem is that the whiskers, 463 00:21:43,120 --> 00:21:45,040 because they're moving back and forth, 464 00:21:45,040 --> 00:21:47,680 they sometimes generate signals in the strain 465 00:21:47,680 --> 00:21:50,770 gauges that are detecting bending of the whisker. 466 00:21:50,770 --> 00:21:52,390 And sometimes those signals, just as 467 00:21:52,390 --> 00:21:55,090 a consequence of the movement and the mass of the whisker, 468 00:21:55,090 --> 00:21:57,550 are strong enough to be above threshold 469 00:21:57,550 --> 00:21:59,570 to generate an orient, so you get, 470 00:21:59,570 --> 00:22:02,350 if you like, these ghost-orienting movements 471 00:22:02,350 --> 00:22:04,990 towards stimuli that don't exist. 472 00:22:04,990 --> 00:22:08,590 And we know that rats don't make these kind of ghost orients, 473 00:22:08,590 --> 00:22:11,920 so something else must be going on in the brain. 474 00:22:11,920 --> 00:22:15,240 And one part of the brain that might be helping here 475 00:22:15,240 --> 00:22:18,780 is a region called the cerebellum, which, I'm not sure 476 00:22:18,780 --> 00:22:21,360 if you've covered that in the summer school, 477 00:22:21,360 --> 00:22:24,180 but the cerebellum, this large structure 478 00:22:24,180 --> 00:22:25,980 at the back of the right brain. 479 00:22:25,980 --> 00:22:29,010 One of its key functions seems to be 480 00:22:29,010 --> 00:22:32,510 to make predictions about sensory signals, 481 00:22:32,510 --> 00:22:35,580 and particularly, to be able to predict sensory signals that 482 00:22:35,580 --> 00:22:37,755 have been caused by your own movement. 483 00:22:37,755 --> 00:22:39,510 And there's a lovely experiment that's 484 00:22:39,510 --> 00:22:43,200 been done by Blakemore et al, where they put people 485 00:22:43,200 --> 00:22:44,760 into a scanner. 486 00:22:44,760 --> 00:22:49,360 And they investigated how they responded to tickling stimuli. 487 00:22:49,360 --> 00:22:51,270 So of course, if somebody tickles you, 488 00:22:51,270 --> 00:22:53,910 that can be quite amusing, but unfortunately, 489 00:22:53,910 --> 00:22:56,940 if you try to tickle yourself, it's really uninteresting. 490 00:22:56,940 --> 00:22:59,670 It doesn't work as a stimulus. 491 00:22:59,670 --> 00:23:04,530 And it's worth thinking about why it is that self-tickling 492 00:23:04,530 --> 00:23:06,150 is so unrewarding. 493 00:23:06,150 --> 00:23:08,910 And one of the reasons is that it's just not surprising. 494 00:23:08,910 --> 00:23:12,160 You know what's going to happen when you tickle yourself, 495 00:23:12,160 --> 00:23:14,070 whereas if somebody else is doing it, 496 00:23:14,070 --> 00:23:16,290 it's unexpected and surprising. 497 00:23:16,290 --> 00:23:20,670 So why is self-tickling unexpected? 498 00:23:20,670 --> 00:23:22,560 Why is it not surprising? 499 00:23:22,560 --> 00:23:25,530 It must be because the brain expects and anticipates 500 00:23:25,530 --> 00:23:27,570 the signal that it's going to get. 501 00:23:27,570 --> 00:23:30,450 And what Blakemore et al did was to show 502 00:23:30,450 --> 00:23:32,910 that the cerebellum really lights up 503 00:23:32,910 --> 00:23:35,220 when you try to tickle yourself, because it's 504 00:23:35,220 --> 00:23:38,340 estimating and predicting the sensory signal, 505 00:23:38,340 --> 00:23:40,920 and using that to cancel out, if you like, 506 00:23:40,920 --> 00:23:44,130 the signal that's coming from your skin. 507 00:23:44,130 --> 00:23:48,030 The same thing is happening in electric fish, which 508 00:23:48,030 --> 00:23:50,490 generate this broad electric field which 509 00:23:50,490 --> 00:23:53,242 they use for catching prey. 510 00:23:53,242 --> 00:23:55,200 And they need to be able to tell the difference 511 00:23:55,200 --> 00:23:58,800 between a distortion to the electric field caused by a prey 512 00:23:58,800 --> 00:24:01,350 animal and a distortion caused by their own movement, 513 00:24:01,350 --> 00:24:02,370 by swimming. 514 00:24:02,370 --> 00:24:05,080 And they do that by having a very large cerebellum. 515 00:24:05,080 --> 00:24:06,780 So we put a model of the cerebellum 516 00:24:06,780 --> 00:24:08,700 in our whiskered robot. 517 00:24:08,700 --> 00:24:11,020 And the cerebellum predicts the noise 518 00:24:11,020 --> 00:24:13,830 you might get due to the movement of the whiskers. 519 00:24:13,830 --> 00:24:15,930 And it learns online to accurately 520 00:24:15,930 --> 00:24:18,150 predict what noise signals you might get, 521 00:24:18,150 --> 00:24:19,920 and to cancel them out, so you get 522 00:24:19,920 --> 00:24:24,420 a much better signal-to-noise ratio in the robot. 523 00:24:24,420 --> 00:24:28,460 So we've dealt with whisking. 524 00:24:28,460 --> 00:24:29,940 And we've dealt with orienting. 525 00:24:29,940 --> 00:24:33,060 But as you saw with that rat on the windowsill, 526 00:24:33,060 --> 00:24:35,291 the whisker movements are really precise. 527 00:24:35,291 --> 00:24:36,540 And they're really controlled. 528 00:24:36,540 --> 00:24:38,550 And the rat seems to really care about 529 00:24:38,550 --> 00:24:41,190 how it's moving its whiskers and how it's touching. 530 00:24:41,190 --> 00:24:43,290 We call this active sensing. 531 00:24:43,290 --> 00:24:45,360 And if you look at these high-speed videos, 532 00:24:45,360 --> 00:24:47,220 you can see, for instance, this rat 533 00:24:47,220 --> 00:24:49,880 when it's exploring this perspex block. 534 00:24:49,880 --> 00:24:53,354 The whiskers aren't moving in a stereotype symmetric way. 535 00:24:53,354 --> 00:24:55,770 You can see that here, the whiskers on the right-hand side 536 00:24:55,770 --> 00:24:57,940 are really reaching round to try and reach 537 00:24:57,940 --> 00:24:59,770 the other side of the block. 538 00:24:59,770 --> 00:25:01,740 If you watch this rat here, you see that too. 539 00:25:01,740 --> 00:25:03,960 You've got asymmetry. 540 00:25:03,960 --> 00:25:07,260 And you'll see that, even as the rat comes up to the cylinder 541 00:25:07,260 --> 00:25:10,710 here, the whiskers at the front are pushing forward 542 00:25:10,710 --> 00:25:14,170 while the ones at the back are hardly moving at all, 543 00:25:14,170 --> 00:25:16,350 so there's some ability to control even the whiskers 544 00:25:16,350 --> 00:25:18,130 on one side of the head. 545 00:25:18,130 --> 00:25:20,064 And when you move your fingers, of course, 546 00:25:20,064 --> 00:25:22,230 there's some coupling between your finger movements. 547 00:25:22,230 --> 00:25:24,510 You can't move them entirely independently. 548 00:25:24,510 --> 00:25:26,760 And each of these whiskers has its own muscle, 549 00:25:26,760 --> 00:25:29,490 so there's a degree of independence 550 00:25:29,490 --> 00:25:31,690 in how the whiskers can move. 551 00:25:31,690 --> 00:25:34,720 And we find that when we record over long intervals. 552 00:25:34,720 --> 00:25:36,290 So this was a study-- 553 00:25:36,290 --> 00:25:37,580 [ELECTRONIC NOISE] 554 00:25:37,580 --> 00:25:42,880 --in which we recorded the whisking muscles using EMG. 555 00:25:42,880 --> 00:25:46,410 And that's the sound that you can hear as the rat explored. 556 00:25:46,410 --> 00:25:50,270 And we tracked the rat as he was moving around. 557 00:25:50,270 --> 00:25:51,920 And we showed that, whenever he came 558 00:25:51,920 --> 00:25:55,060 close to the edge of the box here, 559 00:25:55,060 --> 00:25:56,720 the whiskers would become asymmetric. 560 00:25:56,720 --> 00:25:58,970 And the whiskers that were furthest away from the wall 561 00:25:58,970 --> 00:26:02,120 would push round to try and touch the sides of the box. 562 00:26:02,120 --> 00:26:04,490 The whiskers that were close to the wall 563 00:26:04,490 --> 00:26:06,180 would barely move at all. 564 00:26:06,180 --> 00:26:10,280 So we want to put that kind of control into our robot. 565 00:26:10,280 --> 00:26:14,600 So I briefly want to come back to this question of how 566 00:26:14,600 --> 00:26:17,160 we decompose control. 567 00:26:17,160 --> 00:26:21,470 So in our original robot that was controlled 568 00:26:21,470 --> 00:26:24,050 by the basal ganglia, and it's collecting cans, 569 00:26:24,050 --> 00:26:26,990 we decompose behaviors into the different elements 570 00:26:26,990 --> 00:26:28,120 of behavior-- 571 00:26:28,120 --> 00:26:31,520 looking for a can, picking it up, carrying it to the wall, 572 00:26:31,520 --> 00:26:32,880 these sorts of things. 573 00:26:32,880 --> 00:26:35,180 And if we look in the ethology literature, 574 00:26:35,180 --> 00:26:37,400 we find that people have talked about these kinds 575 00:26:37,400 --> 00:26:38,850 of decompositions. 576 00:26:38,850 --> 00:26:41,330 There's a very famous paper by Baerends 577 00:26:41,330 --> 00:26:42,780 about the herring gull. 578 00:26:42,780 --> 00:26:44,310 And with the herring gull, there's 579 00:26:44,310 --> 00:26:50,090 this famous experiment where the egg rolls out the nest. 580 00:26:50,090 --> 00:26:53,360 And the bird will retrieve the egg with its bill 581 00:26:53,360 --> 00:26:55,220 and push it back into the nest. 582 00:26:55,220 --> 00:26:59,340 And it will do this same action really reliably and repeatedly. 583 00:26:59,340 --> 00:27:01,250 And it can do it with eggs of various size. 584 00:27:01,250 --> 00:27:03,380 It might even do it for a Coke can. 585 00:27:03,380 --> 00:27:05,960 And if you take the egg away during the movement, 586 00:27:05,960 --> 00:27:07,970 it will still complete the movement. 587 00:27:07,970 --> 00:27:11,360 And ethologists have called this a fixed action pattern, 588 00:27:11,360 --> 00:27:13,100 so it may be that behavior is decomposed 589 00:27:13,100 --> 00:27:15,650 into action patterns. 590 00:27:15,650 --> 00:27:17,630 And that's one of the ways, for instance, 591 00:27:17,630 --> 00:27:20,720 in which Rodney Brooks wants to decompose robot behavior. 592 00:27:20,720 --> 00:27:23,030 We decompose it into different things 593 00:27:23,030 --> 00:27:24,605 we might want the robot to do. 594 00:27:24,605 --> 00:27:26,480 And we can do that with our whiskered robots. 595 00:27:26,480 --> 00:27:30,020 Here's another one with its behavior decomposed 596 00:27:30,020 --> 00:27:32,090 into different kinds of, if you like, 597 00:27:32,090 --> 00:27:36,270 orienting behaviors and fixed action patterns. 598 00:27:36,270 --> 00:27:37,940 Another way to decompose behavior 599 00:27:37,940 --> 00:27:40,730 is to think about where your attention is, so 600 00:27:40,730 --> 00:27:43,070 where you put your attention might decide 601 00:27:43,070 --> 00:27:44,540 what you're going to do next. 602 00:27:44,540 --> 00:27:47,180 And for an animal that doesn't have arms, 603 00:27:47,180 --> 00:27:51,080 and of course most animals except humans and some primates 604 00:27:51,080 --> 00:27:54,650 don't usually use their forelimbs for much else 605 00:27:54,650 --> 00:27:56,390 other than locomotion. 606 00:27:56,390 --> 00:27:58,430 And they primarily are positioning 607 00:27:58,430 --> 00:28:00,840 their head and their face. 608 00:28:00,840 --> 00:28:03,600 And their main effector, then, is their mouth. 609 00:28:03,600 --> 00:28:05,930 So where you position your attention 610 00:28:05,930 --> 00:28:08,520 could determine what you're going to do next. 611 00:28:08,520 --> 00:28:10,580 So another way of decomposing control 612 00:28:10,580 --> 00:28:12,977 is to solve the attention problem first. 613 00:28:12,977 --> 00:28:14,685 And then once you solve that, the problem 614 00:28:14,685 --> 00:28:17,180 of what you're going to do is simplified. 615 00:28:17,180 --> 00:28:19,940 So in this robot, we're controlling it 616 00:28:19,940 --> 00:28:22,190 by deciding where its attention should go. 617 00:28:22,190 --> 00:28:26,150 And then the rest of the body kind of follows. 618 00:28:26,150 --> 00:28:28,910 When humans have special attention, of course, 619 00:28:28,910 --> 00:28:31,560 we explore that in the visual modality. 620 00:28:31,560 --> 00:28:34,170 And we look at the saccadic eye movements that people make. 621 00:28:34,170 --> 00:28:37,940 So in the famous experiment, Albert Yarbus 622 00:28:37,940 --> 00:28:39,980 had people looking at this picture 623 00:28:39,980 --> 00:28:41,990 and tracking where their eyes would look. 624 00:28:41,990 --> 00:28:45,470 And of course, we look at the socially-significant elements 625 00:28:45,470 --> 00:28:48,080 of the picture, people's faces and so on, 626 00:28:48,080 --> 00:28:52,750 not just arbitrary points of light, or corners, and so on. 627 00:28:52,750 --> 00:28:57,170 And we can actually calculate a saliency map for space 628 00:28:57,170 --> 00:29:00,140 and say what are the important parts of space 629 00:29:00,140 --> 00:29:02,690 for exploring and attending to. 630 00:29:02,690 --> 00:29:07,370 And we've taken that idea and transferred it into our model 631 00:29:07,370 --> 00:29:08,630 for understanding the rat. 632 00:29:08,630 --> 00:29:11,510 And we thought about tactile saliency maps, so can 633 00:29:11,510 --> 00:29:13,900 we, with a sense of touch, think about areas 634 00:29:13,900 --> 00:29:17,000 of the world which are important to explore and understand 635 00:29:17,000 --> 00:29:18,080 through touch? 636 00:29:18,080 --> 00:29:21,110 And can we use that to control the movement 637 00:29:21,110 --> 00:29:24,470 of our robot, or in this case, our simulation? 638 00:29:24,470 --> 00:29:28,070 So here, we have a form of emergent wall following, which 639 00:29:28,070 --> 00:29:31,850 is a consequence of the rat's spatial attention being 640 00:29:31,850 --> 00:29:35,830 driven by contact with vertical objects, which we-- 641 00:29:35,830 --> 00:29:39,230 we program it so that the vertical surfaces 642 00:29:39,230 --> 00:29:41,090 are salient and interesting. 643 00:29:41,090 --> 00:29:43,130 And it has this salient zone. 644 00:29:43,130 --> 00:29:46,610 And it tries to put its whiskers into the salient zone. 645 00:29:46,610 --> 00:29:49,700 And then here is a robot instantiating this. 646 00:29:49,700 --> 00:29:51,380 This is Ben Mitchinson, who's programmed 647 00:29:51,380 --> 00:29:52,730 many of these robots. 648 00:29:52,730 --> 00:29:54,800 And so what we're doing now is following 649 00:29:54,800 --> 00:29:58,820 this biologically-inspired orienting system 650 00:29:58,820 --> 00:29:59,930 to explore shapes. 651 00:29:59,930 --> 00:30:03,350 And in this case, he put his own face in front of the robot. 652 00:30:03,350 --> 00:30:07,580 And you can see the robot making light touches against his face 653 00:30:07,580 --> 00:30:11,420 and investigating it, looking-- 654 00:30:11,420 --> 00:30:14,090 making a series of, if you like, exploratory touches, 655 00:30:14,090 --> 00:30:17,720 somewhat like saccades, somewhat like what 656 00:30:17,720 --> 00:30:19,670 you might imagine a blind person would 657 00:30:19,670 --> 00:30:21,740 do if they were investigating your face 658 00:30:21,740 --> 00:30:23,300 to try and recognize you. 659 00:30:23,300 --> 00:30:25,310 And Mitra Hartmann from Northwestern 660 00:30:25,310 --> 00:30:27,200 has shown that you can take signals 661 00:30:27,200 --> 00:30:30,040 off these kinds of whiskers and reconstruct a face, 662 00:30:30,040 --> 00:30:33,410 so it should be possible from this 663 00:30:33,410 --> 00:30:36,960 to build up from the touches, the sequence of touches, 664 00:30:36,960 --> 00:30:39,080 a lot of rich information about the object that's 665 00:30:39,080 --> 00:30:40,480 being investigated. 666 00:30:40,480 --> 00:30:42,202 How much time? 667 00:30:42,202 --> 00:30:42,910 I need to finish. 668 00:30:42,910 --> 00:30:44,630 OK, let me just skip through. 669 00:30:44,630 --> 00:30:47,362 So we've been doing-- working on the cortex. 670 00:30:47,362 --> 00:30:48,820 We have a number of models of that, 671 00:30:48,820 --> 00:30:51,580 which I'd like to show you, but I 672 00:30:51,580 --> 00:30:53,950 want to just finish, just to make contact 673 00:30:53,950 --> 00:30:56,980 with John's talk, is that we've been doing, 674 00:30:56,980 --> 00:31:00,640 in our robots, tactile simultaneous localization 675 00:31:00,640 --> 00:31:01,270 and mapping. 676 00:31:01,270 --> 00:31:03,520 So this is our whiskered robot. 677 00:31:03,520 --> 00:31:05,830 And we have various models for this, some of which 678 00:31:05,830 --> 00:31:07,780 are more hippocampal-like. 679 00:31:07,780 --> 00:31:10,130 This one, I think, was more of an engineered model. 680 00:31:10,130 --> 00:31:12,700 But you can see the robot just using touch 681 00:31:12,700 --> 00:31:15,340 on these artificial whiskers, building up 682 00:31:15,340 --> 00:31:17,090 a map of its environment. 683 00:31:17,090 --> 00:31:19,600 These two lines show its dead-reckoning position 684 00:31:19,600 --> 00:31:21,490 and its calculated position. 685 00:31:21,490 --> 00:31:24,550 And just using touch, we can build up 686 00:31:24,550 --> 00:31:28,220 a reasonably accurate map of the world that we're exploring. 687 00:31:28,220 --> 00:31:31,210 So Giorgio will talk about the iCub. 688 00:31:31,210 --> 00:31:33,730 And I just wanted to mention that, in the work we're 689 00:31:33,730 --> 00:31:36,280 doing with Giorgio, we are very much trying 690 00:31:36,280 --> 00:31:38,360 to understand human cognition. 691 00:31:38,360 --> 00:31:41,680 I wrote a short article for New Scientist on the possibility 692 00:31:41,680 --> 00:31:44,640 that robots might one day have selves.