Name
Abstract | ||
|---|---|---|
Effective human-robot interaction requires systems that can accurately infer and predict human intentions. In this paper, we introduce a system that uses stacked denoising autoencoders to perform intent recognition. We introduce the intent recognition problem, provide an overview of deep architectures in machine learning, and outline the components of our system. We also provide preliminary results for our system's performance. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2157689.2157740 | HRI |
Keywords | Field | DocType |
hidden markov models,neural networks,human robot interaction,noise reduction,machine learning | Noise reduction,Computer science,Simulation,Artificial intelligence,Artificial neural network,Hidden Markov model,Human–robot interaction,Machine learning | Conference |
ISSN | Citations | PageRank |
2167-2121 | 6 | 0.51 |
References | Authors | |
2 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| richard kelley | 1 | 137 | 10.00 |
| Liesl Wigand | 2 | 6 | 0.85 |
| Brian Hamilton | 3 | 6 | 0.85 |
| Katie Browne | 4 | 6 | 0.51 |
| Monica N. Nicolescu | 5 | 358 | 40.44 |
| Mircea Nicolescu | 6 | 792 | 55.76 |