4 papers
Force Generative Imitation Learning: Bridging Position Trajectory and Force Commands through Control Technique
Hiroshi Sato, Sho Sakaino, Toshiaki Tsuji
In contact-rich tasks, while position trajectories are often easy to obtain, appropriate force commands are typically unknown. Although it is conceivable to generate force commands…
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…
Motion Generation for Food Topping Challenge 2024: Serving Salmon Roe Bowl and Picking Fried Chicken
Koki Inami, Masashi Konosu, Koki Yamane +6
Although robots have been introduced in many industries, food production robots are yet to be widely employed because the food industry requires not only delicate movements to hand…
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…