Human-robot cooperation is governed by mutual estimation of intentions. Providing intention recognition capabilities to technical systems will enable these to communicate implicitly with their human users. This research is funded by the Collaborative Research Center 588 "Humanoid Robots - Learning and Cooperating Multimodal Robots", established on the 1st of July 2001 by the Deutsche Forschungsgemeinschaft. ISAS is a member of this center since 2004.
Intention recognition at the ISAS is based on a stochastic modeling approach using hybrid dynamic bayesian networks. Bayesian networks are cascaded stochastic models and represent causal relations between situations, intentions, actions and observations explicitily. The focus of our research is on the representation, inference, and learning of non-linear dependencies and hybrid scenarios, which contain continuous as well as discrete random variables.
As a baseline basic human intention recognition skills were quantified in experimental studies in a kitchen setting. On the basis of these experiments an estimator for intention recognition was developed. This estimator achieves recognition results comparable to those of human users in the given setting.
At ISAS we propose the use of a generic model of the human behavior for intention recognition
of varying scales/scopes and complexity.
We developed Hybrid Dynamic Bayesian Networks that allow for nonlinear dependencies
between the variables and closed-form inference. This is very convenient for modeling mixed discrete and
continuous valued domains which are common in the human surrounding.
|Oliver C. Schrempf, Uwe D. Hanebeck,|
A New Approach for Hybrid Bayesian Networks Using Full Densities
Oliver C. Schrempf, Uwe D. Hanebeck,
Evaluation of Hybrid Bayesian Networks using Analytical Density Representations
- Proceedings of the 16th IFAC World Congress (IFAC 2005), Prague, Czech Republic, July 2005.
- Author : Oliver C. Schrempf, Uwe D. HanebeckAbstract
Title : Evaluation of Hybrid Bayesian Networks using Analytical Density Representations
In : Proceedings of the 16th IFAC World Congress (IFAC 2005)
Address : Prague, Czech Republic
Date : July 2005
Additionally, methods for learning the parameters of Hybrid Bayesian Networks and
learning continuous nonlinear dependencies based on sparse kernel (conditional) densities were developed.
In order to scale the proposed modeling approach to large environments,
we propose the use of situation-specific inference.
Other developments include a method for calculating hybrid clusters, a complexity reduction
for restricted types of systems or methods for data association and knowledge modeling.