62 citations · 162 across the 32 of their papers we have counts for
5 papers · 1 filter
VFILC: Accurate Frequency Extrapolations in Imitation Learning via Sampling Frequency ILC
Nozomu Masuya, Toshiaki Tsuji, Sho Sakaino
Conventional neural network (NN)-based imitation learning methods for variable-speed motion either restricted their scope to interpolated speeds, or generated unpredictable motions…
Reproducible Dynamic Parameter Identification for a Low-Cost Robot Arm: A Positive-Definiteness Audit for Model Acceptance
Junji Oaki, Koki Yamane, Koki Inami +1
Dynamic parameter identification of low-cost robot arms is challenging because limited sensing and drivetrain nonidealities can yield models that predict measured torques well but…
Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learning
Koki Yamane, Cristian C. Beltran-Hernandez, Steven Oh +2
Fast execution of contact-rich manipulation is critical for practical deployment, yet providing fast demonstrations for imitation learning (IL) remains challenging: humans cannot d…
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…
Hierarchical Proportion Models for Motion Generation via Integration of Motion Primitives
Yu-Han Shu, Toshiaki Tsuji, Sho Sakaino
Imitation learning (IL) enables robots to acquire human-like motion skills from demonstrations, but it still requires extensive high-quality data and retraining to handle complex o…