@inproceedings{oai:oist.repo.nii.ac.jp:00001402,
 author = {Matsumoto, Takazumi and Choi, Minkyu and Jung, Minju and Tani, Jun},
 book = {第 28 回 日本神経回路学会全国大会 講演論文集, The Proceedings of the 28th Annual Conference of the Japanese Neural Network Society},
 month = {Oct},
 note = {The ability to plan and visualize object manipulation in advance is vital for both humans and robots to smoothly reach a desired goal state. In this work, we demonstrate how our predictive coding based deep visuomotor recurrent neural network (PDVMRNN) can generate plans for a robot to manipulate objects based on a visual goal. A Tokyo Robotics Torobo Arm robot and a basic USB camera were used to record visuo-proprioceptive sequences of object manipulation. Although limitations in resolution resulted in lower success rates when plans were executed with the robot, our model is able to generate long predictions from novel start and goal states based on the learned patterns.},
 pages = {134--135},
 publisher = {Japanese Neural Network Society},
 title = {Generating Goal-directed Visuomotor Plans with Supervised Learning using a Predictive Coding Deep Visuomotor Recurrent Neural Network},
 year = {2018}
}