In this paper, we studied how actions of others can be recognized by the very same mechanisms that generate similar actions. We used action generation mechanisms to sim-ulate actions and compare resulting trajec-tories with the observed one as the action unfolds. Specifically, we modified Dynamic Movement Primitives (DMP) for online action recognition and used them as our action gen-eration mechanism. A human demonstrator applied three different actions on two differ-ent objects. Recordings of these actions were used to train DMPs and test our approach. We showed that our system is capable of on-line action recognition within approximately the first one-third of the observed action with a success rate of over %90. Online capabilities of the...
There is currently a division between real-world human performance and the decision making of social...
We present a hierarchical self-organizing map based system for online recognition of human actions. ...
In this paper we introduce a novel, simple, and efficient method for human action recognition based ...
This thesis investigates how a robot can use action generation mechanisms to recognize the action of...
Abstract — The studies on mirror neurons observed in mon-keys indicate that recognition of other’s a...
The studies on mirror neurons observed in monkeys indicate that recognition of other's actions activ...
Humans have a remarkable tendency to anthropomorphize moving objects, ascribing to them intentions a...
In this thesis the action learning and generation problem on a humanoid robot is studied. Our aim is...
We present an online system for real time recognition of actions involving objects working in online...
© 2017 Elsevier B.V. We present an online system for real time recognition of actions involving obje...
In the area of imitation learning, one of the important research problems is action representation. ...
Imitation learning is considered to be an effective way of teaching humanoid robots and action recog...
With the introduction of cost-effective depth sensors, a tremendous amount of research has been devo...
With the introduction of cost-effective depth sensors, a tremendous amount of research has been devo...
The design of action recognition algorithms often relies on knowledge of the particular problem, whi...
There is currently a division between real-world human performance and the decision making of social...
We present a hierarchical self-organizing map based system for online recognition of human actions. ...
In this paper we introduce a novel, simple, and efficient method for human action recognition based ...
This thesis investigates how a robot can use action generation mechanisms to recognize the action of...
Abstract — The studies on mirror neurons observed in mon-keys indicate that recognition of other’s a...
The studies on mirror neurons observed in monkeys indicate that recognition of other's actions activ...
Humans have a remarkable tendency to anthropomorphize moving objects, ascribing to them intentions a...
In this thesis the action learning and generation problem on a humanoid robot is studied. Our aim is...
We present an online system for real time recognition of actions involving objects working in online...
© 2017 Elsevier B.V. We present an online system for real time recognition of actions involving obje...
In the area of imitation learning, one of the important research problems is action representation. ...
Imitation learning is considered to be an effective way of teaching humanoid robots and action recog...
With the introduction of cost-effective depth sensors, a tremendous amount of research has been devo...
With the introduction of cost-effective depth sensors, a tremendous amount of research has been devo...
The design of action recognition algorithms often relies on knowledge of the particular problem, whi...
There is currently a division between real-world human performance and the decision making of social...
We present a hierarchical self-organizing map based system for online recognition of human actions. ...
In this paper we introduce a novel, simple, and efficient method for human action recognition based ...