Abstract
Inference of human intention may be an essential step towards understanding human actions and is hence important for realizing efficient human-robot interaction. In this paper, we propose the Intention-Driven Dynamics Model (IDDM), a latent variable model for inferring unknown human intentions. We train the model based on observed human movements/actions. We introduce an efficient approximate inference algorithm to infer the human's intention from an ongoing movement. We verify the feasibility of the IDDM in two scenarios, i.e., target inference in robot table tennis and action recognition for interactive humanoid robots. In both tasks, the IDDM achieves substantial improvements over state-of-The-Art regression and classification.
| Original language | English (US) |
|---|---|
| Title of host publication | Robotics: Science and Systems |
| Publisher | MIT Press Journals |
| Pages | 433-440 |
| Number of pages | 8 |
| Volume | 8 |
| ISBN (Print) | 9780262519687 |
| State | Published - 2013 |
| Externally published | Yes |
| Event | International Conference on Robotics Science and Systems, RSS 2012 - Sydney, Australia Duration: Jul 9 2012 → Jul 13 2012 |
Other
| Other | International Conference on Robotics Science and Systems, RSS 2012 |
|---|---|
| Country | Australia |
| City | Sydney |
| Period | 7/9/12 → 7/13/12 |
ASJC Scopus subject areas
- Artificial Intelligence
- Control and Systems Engineering
- Electrical and Electronic Engineering
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Cite this
Wang, Z., Deisenroth, M. P., Ben Amor, H., Vogt, D., Schölkopf, B., & Peters, J. (2013). Probabilistic modeling of human movements for intention inference. In Robotics: Science and Systems (Vol. 8, pp. 433-440). MIT Press Journals.