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Robot, Task. Task, Robot.

Robot, Task. Task, Robot. The morally dubious exploitation of the new slave race. Robot, Game. Game, Robot. Welcoming our creations into the playful embrace of human culture. Why Tasks are Good. General-purpose perception, action = can of worms

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Robot, Task. Task, Robot.

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  1. Robot, Task. Task, Robot. The morally dubious exploitation of the new slave race

  2. Robot, Game. Game, Robot. Welcoming our creations into the playful embrace of human culture

  3. Why Tasks are Good • General-purpose perception, action = can of worms • Nasty, icky, intractably wriggly worms • Task-specific perception, action =tractable! • So generalize by parameterizing across tasks

  4. Task-Parameterized Perception

  5. Social-Pragmatic Approach • Tomasello – Language is how we invite others to experience the world in a particular way • My plan – to implement tasking this way • For each task, we communicate how to carve up world into actions, objects, properties • Given that, robot can perceive, act, decode references, leap tall buildings

  6. Thrun’s CES Language • Leave “gaps” in control program • Completed later from labeled training sets • Example – mapping sonar+odometry to angle • Example – recognizing left hand, right hand • Process can iterate during code development • My plan – leave larger gaps; scriptable interface

  7. Scripting Analogy • Expose interfaces for physical references, sequences Robot exposes interfaces Coder tailors to own nefarious purposes

  8. Referential Indeterminacy “GAVAGAI” Whole object assumption Taxonomic assumption Mutual exclusivity Gaze direction (Quine: Word and Object, 1960)

  9. “Let’s go find the toma!” (Tomasello: Pragmatics of Word Learning, 1997)

  10. Automata learning Sequencing protocol Physical reference protocol Supervised learning

  11. Physical Reference Protocol • Refer to features by manipulating them • Show extremes, alternate, synchronize • Lies on continuum with sequencing protocol • Cache features by associating labels

  12. Sequencing Protocol • Verbal • Fast vocabulary extension • Offline discovery of filler, language model • Small hardwired grammar • Hardwired error correcting protocol

  13. Run recognizer Extract OOV fragments Identify rarely-used additions Identify competition Add to lexicon Remove from lexicon Update lexicon, baseforms Hypothesized transcript N-Best hypotheses Update Language Model

  14. Ow! Basic Object Competence • Visual segmentation is major source of confusion • Use the “Confuse THIS!” strategy :-

  15. Active Segmentation • Unsure where the boundaries of an object lie? • Poke it gently • Thump it savagely • Try to put your flipper beside it • Try to put your flipper behind it • Displace your head • Get human to present it

  16. Conclusions • Target demonstrations • Can’t do porter, so do sorter • Incremental role transfer • Comprehension of search • Component technologies • Functional autonomy?

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