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…
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…
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…
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…