2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), 2015
Inspired by the emotional conditionings performed by the amygdala, we describe a simulated neural... more Inspired by the emotional conditionings performed by the amygdala, we describe a simulated neural network able to learn the meaning of a previously neutral stimulation. A robot using this neural network can learn the conditioning of a non specific sensor activated by the experimentator and its internal state of pain or pleasure. This biologically inspired adaptative and natural way to interact with the robot is tested with a mobile robot learning navigation tasks in a real environment.
Biologically inspired models for navigation use mechanisms like path integration or sensori-motor... more Biologically inspired models for navigation use mechanisms like path integration or sensori-motor learning. This paper describes the use of a proprioceptive working memory to give path integration the potential to store several goals. Then we coupled the path integration working memory to place cell sensori-motor learning to test the potential autonomy this gives to the robot. This navigation architecture intends to combine the benefits of both strategies in order to overcome their drawbacks. The robot use a low level motivational system. Experimental evaluation is done with a robot in a real environment.
2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010
This paper explores the use of a mechanism to auto-regulate the robot behavior in situations of p... more This paper explores the use of a mechanism to auto-regulate the robot behavior in situations of persistent failures. In order to give more autonomy to a mobile robot, a generical frustration mechanism based on the automonitoring of the progress (in terms of goal distance reduction) is studied in different situations and on different parts of the robot architecture. To escape failure situations and deadlocks, the frustration reaction can inhibit the robot navigation strategies, goals or drives.
Classical models of emotions consider either the communicational aspect of emotions (for instance... more Classical models of emotions consider either the communicational aspect of emotions (for instance the emotions conveyed by the facial expressions) or the second order control necessary for survival purpose when the autonomy of the system is an issue. Here, we show the interdependence of communication and meta-control aspects of emotion. We propose the idea that emotions must be understood as a dynamical system linking two controllers: one devoted to social interactions (i.e. communication aspects) and another one devoted to the interactions within the physical world (i.e metacontrol of a more classical controller). Illustrations will be provided from applications involving navigation among di erent goal places according to di erent internal drives or object grasping and avoidance.
The aim of this study is to show how robots learning could be easier and accessible to non expert... more The aim of this study is to show how robots learning could be easier and accessible to non experts if it relies on emotional interactions, more precisely on social referencing abilities, rather than on specialized supervised learning technics. To test this idea, we coupled two systems : a robotic head able to learn to recognize and imitate emotional facial expressions and a mobile robot able to learn autonomous visual navigation tasks in a real environment. Two possible solutions for coupling these two systems are tested. First, the emotional interactions are used to qualify the robot's behavior. The robot shows its ability to learn how to reach a goal-place of its environment using emotional interaction signal from the experimentator. These signals are giving the robot information about the quality of its behavior and allow it to learn place-actions associations to construct an attraction basin around the goal-place. Second, the emotional interactions are used to qualify the robot's immediat environment. The robot shows its ability to learn how to avoid a place of its environment by associating it with the experimentator's anger facial expression. The first strategy allows the experimentator to teach the robot to reach a specific place from anywhere in its environment. However, this strategy takes more learning time than the second strategy that is very fast but seems to be inappropriate to learn to reach a place instead of avoiding it. While these two different strategies achieve satisfactory results, there is no reason why they should be mutually exclusive. In conclusion, we discuss the coupling of both type of learning. Our results also show that relying on the natural expertise of humans in recognizing and expressing emotions is a very promising approach to human-robot interactions. Furthermore, our approach can provide new interesting insights about how, in their early age, humans can develop high level social referencing capabilities from low level sensorimotors dynamics.
2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), 2015
Inspired by the emotional conditionings performed by the amygdala, we describe a simulated neural... more Inspired by the emotional conditionings performed by the amygdala, we describe a simulated neural network able to learn the meaning of a previously neutral stimulation. A robot using this neural network can learn the conditioning of a non specific sensor activated by the experimentator and its internal state of pain or pleasure. This biologically inspired adaptative and natural way to interact with the robot is tested with a mobile robot learning navigation tasks in a real environment.
Biologically inspired models for navigation use mechanisms like path integration or sensori-motor... more Biologically inspired models for navigation use mechanisms like path integration or sensori-motor learning. This paper describes the use of a proprioceptive working memory to give path integration the potential to store several goals. Then we coupled the path integration working memory to place cell sensori-motor learning to test the potential autonomy this gives to the robot. This navigation architecture intends to combine the benefits of both strategies in order to overcome their drawbacks. The robot use a low level motivational system. Experimental evaluation is done with a robot in a real environment.
2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010
This paper explores the use of a mechanism to auto-regulate the robot behavior in situations of p... more This paper explores the use of a mechanism to auto-regulate the robot behavior in situations of persistent failures. In order to give more autonomy to a mobile robot, a generical frustration mechanism based on the automonitoring of the progress (in terms of goal distance reduction) is studied in different situations and on different parts of the robot architecture. To escape failure situations and deadlocks, the frustration reaction can inhibit the robot navigation strategies, goals or drives.
Classical models of emotions consider either the communicational aspect of emotions (for instance... more Classical models of emotions consider either the communicational aspect of emotions (for instance the emotions conveyed by the facial expressions) or the second order control necessary for survival purpose when the autonomy of the system is an issue. Here, we show the interdependence of communication and meta-control aspects of emotion. We propose the idea that emotions must be understood as a dynamical system linking two controllers: one devoted to social interactions (i.e. communication aspects) and another one devoted to the interactions within the physical world (i.e metacontrol of a more classical controller). Illustrations will be provided from applications involving navigation among di erent goal places according to di erent internal drives or object grasping and avoidance.
The aim of this study is to show how robots learning could be easier and accessible to non expert... more The aim of this study is to show how robots learning could be easier and accessible to non experts if it relies on emotional interactions, more precisely on social referencing abilities, rather than on specialized supervised learning technics. To test this idea, we coupled two systems : a robotic head able to learn to recognize and imitate emotional facial expressions and a mobile robot able to learn autonomous visual navigation tasks in a real environment. Two possible solutions for coupling these two systems are tested. First, the emotional interactions are used to qualify the robot's behavior. The robot shows its ability to learn how to reach a goal-place of its environment using emotional interaction signal from the experimentator. These signals are giving the robot information about the quality of its behavior and allow it to learn place-actions associations to construct an attraction basin around the goal-place. Second, the emotional interactions are used to qualify the robot's immediat environment. The robot shows its ability to learn how to avoid a place of its environment by associating it with the experimentator's anger facial expression. The first strategy allows the experimentator to teach the robot to reach a specific place from anywhere in its environment. However, this strategy takes more learning time than the second strategy that is very fast but seems to be inappropriate to learn to reach a place instead of avoiding it. While these two different strategies achieve satisfactory results, there is no reason why they should be mutually exclusive. In conclusion, we discuss the coupling of both type of learning. Our results also show that relying on the natural expertise of humans in recognizing and expressing emotions is a very promising approach to human-robot interactions. Furthermore, our approach can provide new interesting insights about how, in their early age, humans can develop high level social referencing capabilities from low level sensorimotors dynamics.
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Papers by Cyril Hasson