1. SPA Architectures (planning, deliberative)

2. Science & Reality –“As far as the laws of mathematics refer to reality, they are not certain; as far as they are certain, they do not refer to reality” (Einstein) Remember this dealing with formal models and deliberative robotics

3. Artificial Intelligence What is Artificial Intelligence? –“The science of making machines do things that would require intelligence if done by [people]” (Minsky, 1968) One more definition: Are you sure that robot-frog should have human-like intelligence to solve her problems?

4. “Mind & Body” in AI Descartes: –Mind is distinct from body Heidegger: –We function in the world by simply being a part of it Clarke: –“mind, body and world act as equal partners”

5. Classical Artificial Intelligence Physical Symbol System Hypothesis –“Formal symbol manipulation is both a necessary and sufficient mechanism for general intelligent behaviour” (Newell & Simon, 1957) Computational Representational Understanding of Mind –“Thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures” (Thagard, 1996)

6. Classical Artificial Intelligence Shakey [Nilsson, 1969]: -It failed…. -“the principle drawback of the classical view is that explicit reasoning about the effects of low-level actions is too expensive to generate real-time behavior” [Russell & Norvig, 1995]

7. a typical robot Sensors Actuators Processor Sensors The Old School of AI (and Robotics)

8. sense - plan - act (SPA) Consists of 3 linear, repeated steps: –Sense your environment –Plan what to do next by building a world model through sensor fusion, and taking all goals into account -- both short term and long term –Execute the plan through the actuators The predominant robot control mechanism through 1985 Called also sense - think - act (STA) or deliberative or planning

9. robots have many goals A train is about to hit me I want to take a nap I need to inspect these railroad spikes I am about to fall over A goal’s priority naturally will change based on context I just want to be loved

10. Tradition approach to slicing the problem: SPA decomposition by function - classical AI Sensors   Actuators Motor control Task execution Planning Modeling Perception All goals are known at each stage, and affect the computation

11. The Control Cycle: SPA A fundamental methodology Derived in the early days of robotics from engineering principles Sense-plan-act cycle: –the principle is to continuously attempt to minimise the error between the actual state and the desired state based on control theory sense compute (plan) act think

12. perceptioncognitionaction world The Control Cycle: SPA Discuss stages

13. modular horizontal SPA architecture In case of soccer robot this architecture looks as this:

14. Agent design can be for instance like this: –Sequential flow –Percepts are obtained from sensors in world (somehow) –Get a logic-based or formal description of percepts E.g., wumpus world percepts –We apply search operators or logical inference or planning operators General (replaceable) formal goal –Arrive at some operator or operator sequence –Apply that operator sequence to world (somehow) The Control Cycle: SPA

15. Path Generation k = DOF of robot C configuration space of robot(set of points) O configuration space of obstacle F = C - O free space, the set of configurations in which the robot can move safely

16. Path Generation for mobile and stationary robots A workspace with a rotary two- link arm. The goal is to move from configuration c 1 to configuration c 2 The corresponding configuration space, showing the free space and a path that achieves the goal 22 22 c1c1 c2c2 c1c1 c2c2

17. NAVIGATION AND MOTION PLANNING Given analysis of robotics problems as motion in configuration spaces, we will begin with algorithms that handle C language directly (no parallel instructions) These algorithms usually assume that an exact description of the space is available, –so they cannot be used where there is significant sensor error and motion error We can identify five major classes of algorithms, and arrange them roughly in order of amount of information required at planning time and execution time

18. 1. Cell decomposition methods break continuous space into a finite number of cells, yielding a discrete search problem 2. Skeletonization methods compute a one- dimensional “skeleton” of the configuration space, yielding an equivalent graph search problem 3. Bounded-error planning methods assume bounds on sensors and actuator uncertainty NAVIGATION AND MOTION PLANNING: Classes of algorithms

19. 1. Cell decomposition method NAVIGATION AND MOTION PLANNING A vertical strip cell decomposition of the configuration space for a two-link robot. The obstacles are dark blobs, the cells are rectangles and the solution is contained within grey rectangles.

20. 4. Landmark-based navigation methods assume that there are some regions in which the robots location can be pinpointed using landmarks, whereas outside those regions it may have only orientation information 5. Online algorithms assume that the environment is completely unknown initially, although most assume some form of accurate position sensor –Instead, one can try to produce a conditional plan or policy that will make decisions at run time NAVIGATION AND MOTION PLANNING ALGORITHMS CONT.

21. NAVIGATION AND MOTION PLANNING A two-dimensional environment, robot and goal

22. The problem of moving a complex-shaped object( i.e., the robot and anything it is carrying) through a space with complex-shaped obstacles is a difficult one. The mathematical notation of configuration space provides a framework for analysis. Cell decomposition and skeletonization methods can be used to navigate through the configuration space. Both reduce a high dimensional, continuous space to a discrete graph- search problem. Some aspects of the world, such as the exact location of a bolt in the robot’s hand, will always be unknown. Fine-motion planning deals with this uncertainty by creating a sensor- based plan that will work regardless of exact initial conditions. SUMMARY ON NAVIGATION

23. Uncertainty applies to sensors at the large scale as well. In the landmark model, a robot uses certain well- known landmarks in the environment to determine where it is, even in the face of uncertainty. If a map of the environment is not available, then the robot will have to plan its navigation as it goes. Online algorithms do this. we can analyze how far off they will be.They do not always choose the shortest route, but we can analyze how far off they will be. SUMMARY ON NAVIGATION

24. Problems with SPA (sense-plan-act) Its monolithic design makes it slow –At each step, we have to do: sensor fusion, world modeling, and planning for all goals Slow means we almost never can plan at the rate the environment is changing We end up doing “open-loop plan execution” – inadequate in the fact of uncertainty and unpredictability

25. Model- Based Approaches Model- Based Approaches

26. Model Based Architectures A symbolic internal ‘world-model’ is maintained: –the sub-tasks are decomposed into functional layers –similar to ‘classical’ artificial intelligence approach sense perception modelling planning task execution motor control act Many levels assess the model

27. Problems with Models An adequate, accurate and up-to-date model must be maintained at all times –this is very difficult in practice! –suppose, for example, the sensors detect an object that we have not got a symbol for (a novel object) A model-based system is extremely brittle –if one of the functional layers fails (e.g. hardware problems, software bugs), then the whole system fails Significant processing power is required –maintaining the model takes time, so slow responses!? Despite much effort, little progress was made!

28. Problems with traditional approaches Can’t account for large aspects of Intelligence, Reliant on representation Rapidly changing boundary conditions Hard to map sensor values to physical quantities Not robust Relatively slow response Hard to extend Hard to test

29. Sources Rodney Brooks Maja Mataric Nilsson’s book Jeremy Elson Norvig’s book, chapter 2. Good. Stimulus-Response Agents English PH.D thesis, recent Jon Garibaldi Prof. Bruce Donald, Changxun Wu, Dartmouth College Leo Ilkko Prof. Manuela Veloso, Dr. Tucker Balch, and Dr. Brett Browning Carnegie Mellon University Rabih Neouchi, Donald C. Onyango and Stacy F. President Axel Roth Ramon Brena Pinero ITESM Rhee, Taik-heon, Computer Science Department, KAIST Brian R. Duffy, Gina Joue Lucy Moffatt, Univ of Sheffield Yorick Wilks, Computer Science Department, University of Sheffield