1. NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 1 Learning in Worlds with Objects Leslie Pack Kaelbling MIT Artificial Intelligence Laboratory With Tim Oates, Natalia Hernandez, Sarah Finney

2. NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 2 What is an Agent? A system that has an ongoing interaction with an external environment household robot factory controller web agent Mars explorer pizza delivery robot Environment Action Observation

3. NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 3 Agents Must Learn Learning is a crucial aspect of intelligent behavior human programmers lack required knowledge agents should work in a variety of environments agents should work in changing environments What to learn? World dynamics: What happens when I take a particular action? Reward: What world states are good?

4. NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 4 Current state-of-the-art learning methods will not work in domains with multiple objects: These are crucial domains for robots of the future. Crisis ?

5. NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 5 Representation Learning requires some sort of representation of states of the world. The choice of representation affects what information can be represented what kinds of generalizations the agent can make

6. NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 6 Attribute Vector State-of-the-art representation for learning temperature = 48.2 pressure = 57.9 mB valve1 = open valve2 = closed time = 10:48AM backlog = 78 volume = 32.2 production = 45.5 …

7. NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 7 Generalization over Attribute Vectors temp > 22 time < 10AM pressure < 3 close valve increase temp add reagent open valve temp time x

8. NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 8 Complex Everyday Domains book1-on-book2:true book2-on-book1:false pen-is-yellow:true pen-is-blue:false lamp-on:true lamp-off:false ink-bottle-level:50% lamp-in-bottle:false bottle-on-lamp:false paper1-color:gray paper2-color:white fabric-behind-lamp:true book2-is-clear:false book4-is-clear:false book1-is-clear:true block1-on-block2:false block3-unstable:true block2-on-table:false block1-in-front-of-lamp:true … Attribute vector is impossibly big

9. NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 9 Generalization over Objects If book1 is on book2 and I move book2, then book1 will move If the cup is on the table and I move the table, then the cup will move If the pen is on the paper and I move the paper, then the pen will move If the coat is on the chair and I move the chair, then the coat will move For all objects A and B: If A is on B and I move B, then A will move

10. NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 10 Referring to Objects Traditional symbolic AI has the problem of “symbol grounding”: How do I know what object is named by book1? on(book1,book2)

11. NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 11 Deictic Expressions “Deixis” is Greek for “pointing” koko ima watashi-ga motteiru hakowatashi-ga miteiru hako

12. NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 12 Automatic Generalization If I have an object in my hand and I open my hand, then the object that was in my hand is now on the table This is true, no matter what object is in your hand.

13. NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 13 Communicating with Humans Natural language communication speaks of the world in terms of objects and their relationships uses deictic expressions Our robots of the future will have to be able to understand and generate human descriptions of the world

14. NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 14 Long-Term Research Goal A robotic system with hand and cameras that can learn to achieve tasks efficiently through trial and error acquire natural language descriptions of the objects and their properties through “conversation” with humans

15. NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 15 Short-Term Research Plan Explore deictic, object-based representation for learning algorithms build simulated hand-eye robot system that manipulates blocks (with real physics) have simulated robot learn to carry out tasks from trial and error Demonstrate empirically and theoretically that deictic representation is crucial for efficient learning

16. NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 16 First Example Domain Unreliable block stacking: robot is rewarded for making tall piles of blocks the taller a pile is, the more likely it is to fall over when another block is added a pile can be made more stable by building piles to its sides Once the robot learns to do this task, keep the physics of the domain the same, but reward a more complex behavior.

17. NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 17 Learning by Doing Having an initial task to perform focuses the robot’s attention on aspects of the environment Use extension of Utree learning algorithm to select important aspects of the environment Generate new deictic expressions dynamically: the-block-on-top-of(the-block-I-am-looking-at) Extend reinforcement learning methods to apply to object-based representations

18. NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 18 Extracting General Rules There are too many facts that are true in any interesting environment. Solving tasks focuses attention on particular objects (named with deictic expressions) particular properties of those objects These objects and properties are likely of general importance: use them as input to association-rule learning algorithm to learn facts like: The thing that is on the thing that I am holding will probably fall off if I move

19. NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 19 Enabling Planning Given general rules, the agent can “think” about the consequences of its actions and decide what to do, rather than learn through trial and error.

20. NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 20 In Future An ambitious research project vision algorithms for learning segmentation and object recognition learning good properties and relations for characterizing the domain (“concept learning”) connect with natural language learning for word meanings

21. NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 21 Don’t miss any dirt!