1.7 Conclusion
Specific problems were extracted in an experiment using a practical system in an attempt to transport an object cooperatively with two humanoid robots. The result proved that both body swinging during movement and the shift in the center of gravity, by transporting an object, caused a shift in the position after movement.
We investigated the behavior of fundamental motions to make sure the impact of initial positioning on the robot operation. Consequently, it is found that position matching of motors is very difficult even using the same robot and even in the same motion, there occur errors in moving distance and direction.
Therefore, we have proposed a learning method to revise a position shift while the cooperative transportation, and established a learning framework in a simulation. In addition, the obtained results were verified by using real robots in a real environment.
In order to move towards the target position efficiently, it is necessary to perform the real learning by two robots. Therefore, it is important to discuss the approach for efficient movement and perform experiment with real robots. Since huge time is required for learning in real robots, it is important to reduce the time of learning in real environment using learning data in the simulator.
In our future work, we want to study how robots can more to the target in the shortest path when there is an obstacle in the path or how to more in an L-shaped path.
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Inoue, Y., Tohge, T., Iba, H. (2005). Learning for Cooperative Transportation by Autonomous Humanoid Robots. In: Nedjah, N., Mourelle, L.d.M. (eds) Evolvable Machines. Studies in Fuzziness and Soft Computing, vol 161. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32364-3_1
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