SPA Architectures (planning, deliberative)

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 One more definition: • What is Artificial Intelligence? • “The science of making machines do things that would require intelligence if done by [people]” (Minsky, 1968) 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 The Old School of AI (and Robotics) Sensors Sensors Sensors Actuators Processor

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

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

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

12. The Control Cycle: SPA world world perception cognition action Discuss stages

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

14. The Control Cycle: SPA • 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)

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

16. Path Generation for mobile and stationary robots 2 c1 c2 2 c1 c2 A workspace with a rotary two-link arm. The goal is to move from configuration c1 to configuration c2 The corresponding configuration space, showing the free space and a path that achieves the goal

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. NAVIGATION AND MOTION PLANNING: Classes of algorithms • 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

19. NAVIGATION AND MOTION PLANNING 1. Cell decomposition method 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. NAVIGATION AND MOTION PLANNING ALGORITHMS CONT. • 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

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

22. SUMMARY ON NAVIGATION • 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.

23. SUMMARY ON NAVIGATION • 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. • They do not always choose the shortest route, but we can analyze how far off they will be.

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

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

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 BrowningCarnegie 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