32 citations · 88 across the 25 of their papers we have counts for
5 papers · 2 filters
Variable-Speed Teaching-Playback as Real-World Data Augmentation for Imitation Learning
Nozomu Masuya, Hiroshi Sato, Koki Yamane +3
Because imitation learning relies on human demonstrations in hard-to-simulate settings, the inclusion of force control in this method has resulted in a shortage of training data, e…
Variable-Frequency Imitation Learning for Variable-Speed Motion
Nozomu Masuya, Sho Sakaino, Toshiaki Tsuji
Conventional methods of imitation learning for variable-speed motion have difficulty extrapolating speeds because they rely on learning models running at a constant sampling freque…
Error-Feedback Model for Output Correction in Bilateral Control-Based Imitation Learning
Hiroshi Sato, Masashi Konosu, Sho Sakaino +1
In recent years, imitation learning using neural networks has enabled robots to perform flexible tasks. However, since neural networks operate in a feedforward structure, they do n…
Loss Function Considering Dead Zone for Neural Networks
Koki Inami, Koki Yamane, Sho Sakaino
It is important to reveal the inverse dynamics of manipulators to improve control performance of model-based control. Neural networks (NNs) are promising techniques to represent co…
Imitation Learning Inputting Image Feature to Each Layer of Neural Network
Koki Yamane, Sho Sakaino, Toshiaki Tsuji
Imitation learning enables robots to learn and replicate human behavior from training data. Recent advances in machine learning enable end-to-end learning approaches that directly…