Abstract | ||
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As robots are increasingly integrated into daily life, one of the most important roles they will assume is that of collaboratively helping us perform physical tasks. Be it helping us put together furniture, transporting materials, or assisting with food preparation, a system's ability to assess its (and others') skill level regarding the performance of different tasks is essential to achieving efficient scheduling and collaboration. In this paper, we present preliminary work towards an observation-driven modeling approach allowing an agent to autonomously predict the amount of time required for different agents to complete actions. This approach utilizes insights and observations from the developmental psychology and operations research communities to accurately develop agent-personalized skill proficiency models. We demonstrate our model by evaluating its performance at estimating agent performance in a set of common assembly tasks. Our evaluation measures knowledge-transfer via novel task introduction, as well as extrapolation by predicting future performance given previous experience. |
Year | DOI | Venue |
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2014 | 10.1109/DEVLRN.2014.6982979 | ICDL-EPIROB |
Keywords | Field | DocType |
extrapolation,intelligent robots,learning systems,service robots,agent performance estimation,agent-personalized skill proficiency models,assembly tasks,developmental psychology,future performance prediction,knowledge-transfer,observation-driven modeling approach,operations research communities,performance evaluation,physical tasks,robots,skill duration prediction,transfer learning approach,predictive models,estimation,computational modeling,psychology,assembly | Computer science,Scheduling (computing),Transfer of learning,Knowledge transfer,Artificial intelligence,Robot,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.38 | 4 |
Authors | ||
5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Bradley Hayes | 1 | 65 | 9.58 |
Elena Corina Grigore | 2 | 16 | 4.50 |
alexandru litoiu | 3 | 1 | 0.38 |
Aditi Ramachandran | 4 | 21 | 3.80 |
B. Scassellati | 5 | 1735 | 241.11 |