THE AIM OF THIS PROPOSAL IS TO TEST THE HYPOTHESIS THAT A SYSTEM CAN INCREASE SPEED AND ACCURACY AT INFERRING HUMAN INTENTIONS AND INCREASE THE NUMBER OF ROBOTS A SINGLE ASTRONAUT CAN SUPERVISE BY INFERRING A PERSON'S MENTAL STATE FROM THEIR LANGUAGE UTTERANCES ACTIVELY ASKING QUESTIONS WHEN IT IS CONFUSED DRIVEN BY A FORMAL MODEL OF HRI THAT LEARNS A HIERARCHICAL REPRESENTATION FOR PLANNING UNDER UNCERTAINTY CALLED THE HUMAN-ROBOT COLLABORATIVE POMDP. THIS INFERENCE WILL ENABLE A PERSON TO EFFECTIVELY COMMUNICATE COMPLEX REQUIREMENTS AND TASKS CONSTRAINTS TO THE PERSON AT VERY ABSTRACT LEVELS (E.G. ``INSPECT THE ISS'') AND AT VERY SPECIFIC LEVELS (E.G. ``MOVE LEFT SIX INCHES '') AS WELL AS ASKING TARGETED QUESTIONS AT DIFFERENT LEVELS OF ABSTRACTION (E.G. ``DO YOU MEAN THE PHILIPS SCREWDRIVER OR THE FLAT HEAD?''). THE END GOAL OF THIS PROPOSAL IS TO ACHIEVE SEAMLESS HUMAN-ROBOT COOPERATION ON COMPLEX TASKS APPROACHING THE EASE AND ACCURACY OF HUMAN-HUMAN COLLABORATION. HUMANS COMMUNICATING WITH OTHER HUMANS USING LANGUAGE TO EXPRESS VERY ABSTRACT GOALS AS WELL AS VERY LOW-LEVEL CONCRETE COMMANDS. THIS USE OF LANGUAGE ENABLES HIGH LEVELS OF AUTONOMY BUT ALSO SUPPORTS FLEXIBLE AND FLUID CORRECTIONS AND REPLANNING. ROBOTS THAT CAN USE FLUID LANGUAGE AT MULTIPLE LEVELS OF ABSTRACTION CAN FLEXIBLY RESPOND TO A PERSON'S REQUESTS. THE ULTIMATE IMPACT IS A WORLD WHERE ROBOTS ACTIVELY INTERPRET A PERSON'S INSTRUCTIONS ASKING QUESTIONS WHEN THEY ARE CONFUSED AND ASKING FOR HELP WHEN THEY ENCOUNTER A PROBLEM. THIS PROPOSAL WILL ASSESS PERFORMANCE USING TWO TASKS: NATURAL LANGUAGE CONTROL OF AN AR DRONE UAV (AS A MODEL FOR ASTROBEE) AND NATURAL LANGUAGE-BASED COLLABORATIVE ASSEMBLY USING BAXTER (AS A MODEL FOR ROBONAUT). THESE TASKS WILL ASSESS THE ABILITY OF THE FRAMEWORK TO FACILITATE IMPROVED HUMAN-ROBOT COLLABORATION USING LANGUAGE-BASED INTERFACES AND DECISION-THEORETIC PLANNING.