TY - GEN
T1 - Challenges to decoding the intention behind natural instruction
AU - Torres Peralta, Raquel
AU - Kaochar, Tasneem
AU - Fasel, Ian R.
AU - Morrison, Clayton T.
AU - Walsh, Thomas J.
AU - Cohen, Paul R.
PY - 2011
Y1 - 2011
N2 - Currently, most systems for human-robot teaching allow only one mode of teacher-student interaction (e.g., teaching by demonstration or feedback), and teaching episodes have to be carefully set-up by an expert. To understand how we might integrate multiple, interleaved forms of human instruction into a robot learner, we performed a behavioral study in which 44 untrained humans were allowed to freely mix interaction modes to teach a simulated robot (secretly controlled by a human) a complex task. Analysis of transcripts showed that human teachers often give instructions that are nontrivial to interpret and not easily translated into a form useable by machine learning algorithms. In particular, humans often use implicit instructions, fail to clearly indicate the boundaries of procedures, and tightly interleave testing, feedback, and new instruction. In this paper, we detail these teaching patterns and discuss the challenges they pose to automatic teaching interpretation as well as the machine-learning algorithms that must ultimately process these instructions. We highlight the challenges by demonstrating the difficulties of an initial automatic teacher interpretation system.
AB - Currently, most systems for human-robot teaching allow only one mode of teacher-student interaction (e.g., teaching by demonstration or feedback), and teaching episodes have to be carefully set-up by an expert. To understand how we might integrate multiple, interleaved forms of human instruction into a robot learner, we performed a behavioral study in which 44 untrained humans were allowed to freely mix interaction modes to teach a simulated robot (secretly controlled by a human) a complex task. Analysis of transcripts showed that human teachers often give instructions that are nontrivial to interpret and not easily translated into a form useable by machine learning algorithms. In particular, humans often use implicit instructions, fail to clearly indicate the boundaries of procedures, and tightly interleave testing, feedback, and new instruction. In this paper, we detail these teaching patterns and discuss the challenges they pose to automatic teaching interpretation as well as the machine-learning algorithms that must ultimately process these instructions. We highlight the challenges by demonstrating the difficulties of an initial automatic teacher interpretation system.
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U2 - 10.1109/ROMAN.2011.6005273
DO - 10.1109/ROMAN.2011.6005273
M3 - Conference contribution
AN - SCOPUS:80052996110
SN - 9781457715716
T3 - Proceedings - IEEE International Workshop on Robot and Human Interactive Communication
SP - 113
EP - 118
BT - 2011 RO-MAN - 20th IEEE International Symposium on Robot and Human Interactive Communication, Symposium Digest
T2 - 20th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN 2011
Y2 - 31 July 2011 through 3 August 2011
ER -