**5. Conclusion**

206 The Future of Humanoid Robots – Research and Applications

Some researchers are inrested in transferring skills and behaviors between robots. The skill transfer between robots does not implemented in this paper. However, it is still can be

Assume that ISAC is asked to transfer the skills to another robot, named Motoman. ISAC demonstrates the behavior sequences to Motoman strictly follows the demonstrations from the human teacher. Therefore, there are three demonstrations to reach, grasp and move the

Fig. 15. ISAC demonstrates the behavior sequences with the knight at different locations

tasks space, which is the movements of the right hand of ISAC, to the joint space.

right arm of ISAC and the temporal information related to the sampling points.

The only difference between the observation by ISAC and Motoman is that Motoman should observe the demonstrations using the camera and convert the recorded data in the

O�� � �O�� ��, which is a ��� matrix, records the position values of the end-effector on the

P�� � �P�� �� is still a ��� matrix recording the positions of the Knight on the chess board in the Cartesian space in the demonstration and the temporal information related to the

After the observation, Motoman uses the same process as described in former paragraphs to segment, recognize, and generates new behaviors in simialr but slightly different situations. In the future, our lab intend to implemente the skill stransfer using this framework and

The existing problem is this cognitive framework largely relies on the vision system. In this paper, we proposed a cognitive segmentation method using the visual information from the cameras. As known, vision system is not very stable, and it is easy to be affected by the environmental issues, e.g., light, perspective, and noises. Therefore, how to design a stable vision system is crucial for robotics research, especially for humanoid robots and cognitive

�� � �������������������O��� (13)

Skill Transfer is divided into two parts: demonstration and observation.

incorporated in this cognitive architecture.

Knight.

sampling points

robots[Tan and Liao, 2007a].

cognitive architecture described in this paper.

This paper proposes a cognitive framework to incorporate cognitive segmentation and the DMP algorithm in a cognitive architecture to deal with generating new behaviors in similar but different situations using imitation learning method. The simulation and experimental results demonstrates that this method is effective to solve basic problems in imitation learning when the task constraints were changed a lot.

The main contribution of this paper is that it provides a framework and architecture for robots to complete some complicated tasks, especially in the situation where several task constraints have been changed. A cognitive segmentation method is proposed in this paper. And the experimental resutls demonstrates that the integration of robotic cognitive architectures with the imitation learning technologies is successful.

Basically, the current research in imitation learning for robots is still a control problem in which the sensory information increases largely. Cognitive robots should understand the target of the task, incorporate the perceptual information, and use cognitive methods to generate suitable behaviors in a dynamic environment. This paper provides a possible solution, which can be used in different cognitive architectures, for the future cognitive behavior generation.
