Physically Grounded CBR

1. Physically Grounded CBR By ConstantinSavtchenko

2. CBR As We Know It • So far, we’ve looked at • Expert systems • Make good decisions and solutions in one specific domain • Recommender Systems • Aggregate data and suggest similar cases • Game AI • Make in-game decisions in order to beat an opponent • Overarching property: abstract domains!

3. Physically Grounded CBR • The physical world is the domain • Requires the ability to measure the current state of environment • Distances • Colors • Temperature • Bumpers • The field of robotics operates in this domain, and some applications use CBR

4. Intro to Mobile Robotics • We’ve already discussed some basic robotics with Prof. Munoz (ie. factory automation) • Now we look at autonomous mobile robotics • Autonomous • Mobile • Three key questions • Where am I? • Where am I going? • How do I get there? • A CBR has been applied to answer each question!

5. Basics of Mobile Robotics • Typically need the following 4 states to answer the three questions Localization (Where am I?) Cognition (Whats the next step?) Perception (What do I see?) Motor Control (How do I move?) Real World

6. Difficulties Of Mobile Robotics • Uncontrollable nature of the world • Imperfect measurements • Incorrect perception • Incorrect movements • Infinitely large domain • How do we account for everything? • Split second decisions • There is no time to debate

7. Solving The Difficulties Of Mobile Robotics • Uncontrollable nature – make predictions • Imperfect measurements • Use a priori knowledge • Take multiple measurements • Infinitely large domain – make generalizations • Real time decisions – take shortcuts/heuristics • With these basics in mind, we can begin to represent our problems

8. Designing a CBR System for Mobile Robotics • Case Representation – The hardest and most important part • How should we define cases? Domain is infinite and time based • How do we determine case similarity in continuous environments? • Metrics we know • Logic to reduce case retrieval overhead • What is the scope of my solution? • Low level action? • High level mentality? • Success of Solution • How do we judge the success of the solution? • Autonomous Robotics – no operator to guide • Low tolerance for bad decisions • Split second decisions • What part of mobile robotics are we going to deal with?

9. CBR - Where Am I? • A CBR system was created to use landmarks to describe unknown environments • By using a priori knowledge, robot can make informed decisions • Moving into a corner • Predicting walls • Similarity function used landmarks between current situation and cases • Ros, R. Lopez de Mantaras, R. Sierra, C. Arcos, J, L. A CBR System for Autonomous Robot Navigation. Spanish council for Scientific Research.

10. CBR – Where Am I Going • AIBO Robotic dogs are used to compete in Robocup • Soccer for robotic dogs • Case-base reasoning usedto determine game playswhich are high level actiondecisions • Dribble and shoot

11. AIBO Robocup – The Nitty Gritty • Case Description • Robot Position • Ball Position • Defending Goal • Teammate Position • Opponent Position • Current Time • Score difference • Solution Description • Sequence Of Actions • Similarity Metrics • Gaussian distribution of any real values (a) • Simple symbolic equivalence for goal (b) • A piecewise function to describe the strategy (c) Controllable Not Controllable Not Controllable Controllable Not Controllable Not Controllable Not Controllable

12. AIBO Robocup – Making A Decision • Case Retrieval • Find similar cases based on non controllable features. • Important design decision, single out cases that are ABSOLUTELY similar. Then work from there • So what’s the points of controllable features? • Minimize number of cases • Create a cost function between cases – how much work would it take to modify my current situation? • Case Adaptation – Cost • Measures the difference between controllable euclidean distances (team robot’s positions, in the current situation and the case) • Maximize similarity but minimize cost • Notice that we move away from picking the best decision, and towards the more efficient – This helps nullify any consequences of a bad solution

13. CBR – Where am I going?Advanced Strategies • That didn’t seem too high level, it was just smart planning… Right! But it was a great start. • Case representation was modified to give cases “scope” or inherent similarity based on a “general area” • Actions were changed to involve multiple robots performing sequences of low level actions • Robot 1: dribble, pass to Robot 2Robot 2: move forward, wait, shoot • Working with the basics from before, the research group changed their CBR to implement team-strategies! • A huge jump in logic is made in a very simple manner since the base CBR system already existed!

14. Changes in the Basic CBR System • A Knowledge Base is added, this is heuristically important information, that helps find cases • Instead of comparingactual positions, focuson general positions.We are more interested in qualitative positions. Thus (a) and (b) are actually the same situation • When making a decision, maximize similarity, minimize cost AND maximize number of players in game play

15. Gaining A Lot By Doing a Little • The perfect situation! • Using previous basic technology, simple modifications were made to create very high level strategies • Solutions are sequences of (robot, action) pairs. • Actions are still very basic • Results?

16. CBR - How do I get there? • We just saw a great example of how to discretize our time based on input of a continuous domain. • What happens when we summarize the last 5 seconds? • We need to make instant decisions, as we move, can’t summarize! • Continuous Case Base Reasoning • Continuous representation – cannot discretize • Continuous performance – Decisions must be made at any moment • Continuous Adaptation and Learning – An autonomous agent must adapt as problems get more difficult • To achieve these requirements, we can’t use discrete symbolic reasoning systems.

17. CBR – Mobile Robotic Navigation • Basics of Navigation: • Successfully navigating from the current location to a goal location in an obstacle ridden terrain • Do we plan ahead of time? • How much should we plan for? • What happens if we do not foresee everything? • Do we just wing it and react as things come up? • How do we know we’re not going in circles? • How do we know we didn’t just make an even worse decision? • Can we put the two systems together? • Yes! Lets put low level behaviors together into groups of behavior assemblages: • Aggressive behaviors together, move quickly with a high goal attraction • Cautious behaviors together, move slowly cautiously move around obstacles

18. Behavioral Navigation • Reactive: • Behaviors is determined by parameters • Limit on how close we can get to an obstacle • Limit on speed • Planning: • Modify parameters slowly as we navigate,but in a suddenly new environment, make a huge change! • Navigate through doorway, then you’re outside • Results: • A general plan on how to handle different environments which adjusts as new input comes in. • Requires us to continuously be making changes. Entercontinuous case representations

19. Continuous Case Base Reasoning • How do you describe a continuous case!?! • Make a vector of the value of each continuous value in terms of time. • Similarity • Similarity measured by mean squareddifference between vector representationof the environment.

20. CBR And Perception • VADER Lab: The Smart Wheelchair Project • Create a wheelchair that can navigate environments autonomously given a destination command • How to avoid people 101 • See some object • Determine if it’s a person • Determine motion path if it is a person

21. Determining People Using Case-Based Reasoning • People are not the same shape, nor do they look the same from all sides • Makes a non unimodal distribution • Difficult to define everything possible and react to each situation • The measurements of people is in continuous space • Difficult arises when setting thresholds. • High overlap between people and non-people • Solution: Nearest Neighbors • Can deal with non unimodal data • Decisions can be done on similarity rather than thresholds

22. Advantages of Case-Base Reasoning • Easy to modify case representation and instantly see results • Many applications only use retrieve and reuse, however, easy and beneficial to implement retain • The more retained, the more accurate the reasoner • Very simple implementation, less time spent coding, more on research of features. • Quick and simple high level decisions that use groups or assemblages of low level actions/behaviors • Easy to understand and maintain behavior of robots due to representation of cases

23. Disadvantages of CBR in Mobile Robotics • Very high-level solutions. Robotics typically requires a very good set of low level, PRECISE, solutions • Can be rigid at first until the Case Base and actions have been expanded upon.

24. Bibliography • Ram A. & Santamaria J.C. Continuous Case-Based Reasoning. In Proc. Of the 1993 AAAI Workshop on Case-Based Reasonings, pp. 86-93, Washington, DC, 1993 • Ros R., Lopez de Mantaras R., sierra C., Arcos J.L. A CBR system for autonomous robot navigation. From Artificial Intelligence Research Institute. • Ros R., Veloso M., Lopez de Mantaras R., Sierra C., Arcos J.L. Retrieving and Reusing Game Plays for Robot Soccer. In Roth-Berghofer, T.R., Goker, M.H., Guvenir, H.A. (eds.) ECCBR 2006. LNCS (LNAI), vol. 4106, pp. 47–61. Springer, Heidelberg, 2006 • Ros R., Lopez de Mantaras R., Arcos J.L., Veloso M. Team Playing Behavior in Robot Soccer: A Case-Based Reasoning Approach. In R.O. Weber and M.M. Richter (Eds.): ICCBR 2007, LNAI 4626, pp. 46–60, 2007