[show abstract][hide abstract] ABSTRACT: In the near future, robots are expected to play active roles in human communities. After this time arrives, robots will need to be socially accepted by the people in the communities to which they belong. However, it remains unknown what issues must be resolved to make robots socially accepted. In this paper, we point out the three criteria with which people in communities evaluate a robot and three types of relations people have with the robot. We conducted a six-week study in an office to statistically test the relationship between the evaluations and how well a robot is accepted depending on relation types. We probe that by improving behaviors in the criteria the robot will be more accepted by each type of people. Then, the discussion are presented about the most important issues regarding the social acceptance of robots.
Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on; 10/2008
[show abstract][hide abstract] ABSTRACT: Human beings subconsciously adapt their behaviors to a communication partner in order to make interactions run smoothly. In human-robot interactions, not only the human but also the robot is expected to adapt to its partner. Thus, to facilitate human-robot interactions, a robot should be able to read subconscious comfort and discomfort signals from humans and adjust its behavior accordingly, just like a human would. However, most previous research works expected the human to consciously give feedback, which might interfere with the aim of interaction. We propose an adaptation mechanism based on reinforcement learning that reads subconscious body signals from a human partner, and uses this information to adjust interaction distances, gaze meeting, and motion speed and timing in human-robot interactions. The mechanism uses gazing at the robot's face and human movement distance as subconscious body signals that indicate a human's comfort and discomfort. A pilot study with a humanoid robot that has ten interaction behaviors has been conducted. The study result of 12 subjects suggests that the proposed mechanism enables autonomous adaptation to individual preferences. Also, detailed discussion and conclusions are presented.
IEEE Transactions on Robotics 09/2008; · 2.57 Impact Factor
[show abstract][hide abstract] ABSTRACT: In this paper, we propose natural reflexive behaviors for wheeled inverted pendulum type humanoid robots and show that such behaviors change human impressions of the robot. To achieve human-like communication between humans and robots, the latter need to be recognized by the former as communication partners. We believe that the natural reflexive behaviors of robots play an important role in being human partners. To validate the evidence, we generate four types of reflexive robot behaviors when pushed by someone and conducted preliminary experiments with subjects. We verified that people change their impressions of extroversion, agreeableness, intellect, and neuroticism for the robot by changing the reflexive behaviors
Robot and Human Interactive Communication, 2006. ROMAN 2006. The 15th IEEE International Symposium on; 10/2006
[show abstract][hide abstract] ABSTRACT: In this paper, we propose an adaptation mechanism for robot behaviors to make robot-human interactions run more smoothly. We propose such a mechanism based on reinforcement learning, which reads minute body signals from a human partner, and uses this information to adjust interaction distances, gaze meeting, and motion speed and timing in human-robot interaction. We show that this enables autonomous adaptation to individual preferences by an experiment with twelve subjects.
Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on; 09/2005
[show abstract][hide abstract] ABSTRACT: This paper proposes a subjective map representation that enables a multiagent system to make decisions in a dynamic, hostile environment. A typical situation can be found in the Sony four-legged robot league of the RoboCup competition [M. Veloso, et al., 1998]. The subjective map is a map of the environment that each agent maintains regardless of the objective consistency of the representation among the agents. Owing to the map's subjectivity, it is not affected by incorrect information belonging to other agents. For example, it is not affected by non-negligible errors caused by dynamic changes in the environment, such as falling down or being picked up and brought to other places by the referee. A potential field is defined on the subjective map in terms of subtasks, such as approaching and shooting the ball, and the field is dynamically up-dated so that the robot can decide what to do next. This method is compared with conventional methods that involve sharing or not sharing information.
Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on; 11/2003
[show abstract][hide abstract] ABSTRACT: Visual attention is one of the most important issues for a vision guided mobile robot. Methods have been proposed for visual attention control based on information criterion. However, the robot had to stop walking for observation and decision. This paper presents a method which enables observation and decision more efficiently and adaptively while it is walking. The method uses the expected information gain from future observations for attention control and action decision. It also proposes an image compensation method to handle the image changes due to the robot motion. Both are used to estimate observation probabilities from the observation while it is walking and then action probabilities are estimated from a decision tree based on the information criterion. The method is applied to a four legged robot. Discussions on the visual attention control in the method and the future issues are given.
Intelligent Robots and Systems, 2002. IEEE/RSJ International Conference on; 02/2002
[show abstract][hide abstract] ABSTRACT: Visual attention is one of the most important issues for a vision
guided mobile robot not simply because visual information brings a huge
amount of data but also because the visual field is limited, therefore
gaze control is necessary. The paper proposes a method of sensor space
segmentation for visual attention control that enables mobile robots to
realize efficient observation. The efficiency is considered from a
viewpoint of not geometrical reconstruction but unique action selection
based on information criterion regardless of localization uncertainty.
The method builds a decision tree based on the information criterion
while taking the time needed for observation into account, and attention
control is done by following the tree. The tree is rebuilt by
introducing contextual information for more efficient attention control.
The method is applied to a four legged robot that tries to shoot a ball
into the goal. Discussion on the visual attention control in the method
is given and the future issues are shown
Intelligent Robots and Systems, 2001. Proceedings. 2001 IEEE/RSJ International Conference on; 02/2001