<p>Behaviors.ai (Behaviors.ai is an Engine enHancing verbAl and nonVerbal InteractiOns of RobotS, based on Artificial Intelligence) is a Common Laboratory funded by the ANR (French Research Agency) and gathers skills from LIRIS researchers in Artificial Intelligence and from Hoomano, a specialized firm in software development for social robots. It has started in February 2017 and will last three years.
Behaviors.ai targets to transfer fundamental research results, especially in developmental learning, to mass-market applications in robots. The team research focuses on the understanding of the interactional context and on providing an appropriate and accurate response.</p>
<p>The objectives of Behaviors.ai are to improve the context perception and the accuracy of the given response to provide more empathic human-robots interactions. This research project targets to develop an interactional engine that will include some of the state of the art methods and new innovative solutions, especially based on promising results in developmental learning and in cognitive robotics. It is part of the innovative development of Hoomano consisting in providing generic tools that can be deployed on any robotic platform to become a standard engine on the market. In addition to technical issues related to the diversity of robotic platforms, this objective raises a key research challenge: the engine has to be able to adapt dynamically to the interaction capabilities of the robot and to mature continuously depending on the effective and interesting interactions with its environment.</p>
</div>
</div>
<divclass="visu-description">
<h1>About Lavizu</h1>
<p>Lavizu has been developped by Laurianne Charrier, Paul Moncuquet and Dorian Goepp, and is a part of Behaviors.ai project. This visualisation aims to explain what is happening in the head of the agent when it is learning. Showing the mental states, the memory and the evolution of knowledge of the agent may lead to a better comprehension of the developmental learning algorithms for our team, researchers or the general public. Being able to debug our algorithms to improve their efficiency is also a goal of this visualisation, and could finally lead to more natural interactions with robots.</p>
</div>
<h1>About Behaviors.ai</h1>
<imgsrc="images/behaviors-work.jpg"
alt="team-photo"
key="team-photo"
id="team-photo"/>
<p>
Behaviors.ai (Behaviors.ai is an Engine enHancing verbAl and nonVerbal InteractiOns of RobotS, based on Artificial Intelligence) is a Common Laboratory funded by the ANR (French Research Agency) and gathers skills from LIRIS researchers in Artificial Intelligence and from Hoomano, a specialized firm in software development for social robots. It has started in February 2017 and will last three years.
Behaviors.ai targets to transfer fundamental research results, especially in developmental learning, to mass-market applications in robots. The team research focuses on the understanding of the interactional context and on providing an appropriate and accurate response.
</p>
<p>
The objectives of Behaviors.ai are to improve the context perception and the accuracy of the given response to provide more empathic human-robots interactions. This research project targets to develop an interactional engine that will include some of the state of the art methods and new innovative solutions, especially based on promising results in developmental learning and in cognitive robotics. It is part of the innovative development of Hoomano consisting in providing generic tools that can be deployed on any robotic platform to become a standard engine on the market. In addition to technical issues related to the diversity of robotic platforms, this objective raises a key research challenge: the engine has to be able to adapt dynamically to the interaction capabilities of the robot and to mature continuously depending on the effective and interesting interactions with its environment.
</p>
<h1>About Lavizu</h1>
<p>
Lavizu has been developped by Laurianne Charrier, Paul Moncuquet and Dorian Goepp, and is a part of Behaviors.ai project. This visualisation aims to explain what is happening in the head of the agent when it is learning. Showing the mental states, the memory and the evolution of knowledge of the agent may lead to a better comprehension of the developmental learning algorithms for our team, researchers or the general public. Being able to debug our algorithms to improve their efficiency is also a goal of this visualisation, and could finally lead to more natural interactions with robots.
