Stochastic approach on a simplified OCC model for uncertainty and believability.
ABSTRACT As robots step into the human's daily lives, interaction and communication between human and robot is becoming essential. For this social interaction with humans, we propose an emotion generation model considering simplicity, believability and uncertainty. First, OCC model is simplified and then stochastic approach on emotion decision algorithm for believability and uncertainty is applied. The proposed model is implemented on a 3D robot expression simulator that can express emotions through its facial expression, gesture, led and so on. A demo of the model is provided as a result.
- SourceAvailable from: northwestern.eduEmotions in Humans and Artifacts, edited by R. Trappl and P. Petta and S. Payr, 01/2003: chapter 6: pages 189-211; The MIT Press.
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ABSTRACT: Bayesian Networks are an important area of research and application within the domain of Artificial Intelligence. This paper explores the nature and implications for Bay esian Networks beginning with an overview and comparison of inferential statistics with Bayes’ Theorem. The nature, relevance and applicability of Bayesian Network theory for issues of advanced computability form the core of the current discussion. A number of current applications using Bayesian networks are examined. The paper concludes with a brief discussion of the appropriateness and limitations of Bayesian Networks for human-computer interaction and automated learning.10/2008: pages 117-130;
Conference Proceeding: Generating natural motion in an android by mapping human motion[show abstract] [hide abstract]
ABSTRACT: One of the main aims of humanoid robotics is to develop robots that are capable of interacting naturally with people. However, to understand the essence of human interaction, it is crucial to investigate the contribution of behavior and appearance. Our group's research explores these relationships by developing androids that closely resemble human beings in both aspects. If humanlike appearance causes us to evaluate an android's behavior from a human standard, we are more likely to be cognizant of deviations from human norms. Therefore, the android's motions must closely match human performance to avoid looking strange, including such autonomic responses as the shoulder movements involved in breathing. This paper proposes a method to implement motions that look human by mapping their three-dimensional appearance from a human performer to the android and then evaluating the verisimilitude of the visible motions using a motion capture system. This approach has several advantages over current research, which has focused on copying a person's moving joint angles to a robot: (1) in an android robot with many degrees of freedom and kinematics that differs from that of a human being, it is difficult to calculate which joint angles would make the robot's posture appear similar to the human performer; and (2) the motion that we perceive is at the robot's surface, not necessarily at its joints, which are often hidden from view.Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on; 09/2005