Probabilistic Robotics. Sebastian Thrun, Wolfram Burgard, and Dieter Fox. (2005, MIT Press.) 647 pages

Probabilistic Robotics. Sebastian Thrun, Wolfram Burgard, and Dieter Fox. (2005, MIT Press.) 647 pages. It’s a wild world out there. The most striking pattern one can observe in the history of robotics (since its beginnings in the 1950s) is its staggering successes in completely revolutionizing heavy industry through automation, and its equally spectacular failure to produce robots that work alongside us in the home, or out of doors. Like the artificial life community, roboticists have struggled to develop ways to enable their creations to deal with the constantly changing demands of the real world. In the first attempts, robots were provided with internal models crafted for them by their creators, but this limited their usefulness: They slowly evaluated their options against these models, and became useless (or dangerous) if their models became inaccurate through environmental change. One of the very first autonomous robots used this approach: Shakey the Robot [1], developed in the late 1960s, could reason using internal models, but it shook and hesitated as it used them to plan actions. In the 1980s Rod Brooks of MIT fomented a rebellion in the field by stating that robots may not require models at all in order to exhibit useful behavior [2], and (along with others that followed) loosed upon the research community a series of scrambling, bounding, and otherwise fast-moving robot critters. The debate between classical robotics and behavior-based robotics continues today. With the release of Probabilistic Robotics, Sebastian Thrun and his coauthors have laid down another, equally large gauntlet. If the only constancy in life is change, then robots should be able to deal with the uncertainties around them by taking them into account when operating. Thrun et al. replace the complete (and therefore computationally intensive) internal models from classical robotics with statistical ones that honestly reflect the uncertainty out there in the world, from the point of view of a robot. With precision, elegance, and depth, the authors indicate the deep philosophical and methodological differences that distinguish deterministic and model-free robotics from probabilistic robotics. (It is interesting to note that Thrun is currently director of the artificial intelligence laboratory at Stanford University, which, coincidentally, is also the birthplace of Shakey and where Brooks received his Ph.D.) As the authors state at the outset of the book, uncertainty does not just surround the robot in the form of environmental noise, but exists within the robot as well. Any real-world robot can safely assume that there are gaps in its knowledge about the environment (is this a door I’m seeing? ), but also in regard to its sensors (how well did that last measurement actually indicate that the door is open? ), the effect of an action (did I actually close the door? ), and its local position within a larger environment (have I seen this door before? ). Chapter by chapter, the authors systematically reveal the challenges related to equipping a robot with the wherewithal to deal with increasing uncertainty, and along the way introduce algorithms designed to handle them. This book, however, is not for the faint of heart, as the mathematics required to model and contain this uncertainty may be daunting for some. This book serves well as a graduate textbook, and as a mandatory reference and handbook for those working in the field. That being said, the authors provide a very thorough treatment of the mathematics required in the first part of the book. For those who are not working directly in robotics, such as artificial life researchers, machine learning researchers, and biologists, these initial chapters alone are invaluable as an accessible introduction to probability theory. The structure of the book is excellent, as the mathematics is interspersed with examples provided at varying levels of detail, from simple ‘‘imagine a robot attempting to . . . ’’ scenarios, to visual