Zobrazeno 1 - 10
of 579
pro vyhledávání: '"Morimoto, Jun"'
While MPC enables nonlinear feedback control by solving an optimal control problem at each timestep, the computational burden tends to be significantly large, making it difficult to optimize a policy within the control period. To address this issue,
Externí odkaz:
http://arxiv.org/abs/2410.04929
The simulation-to-real gap problem and the high computational burden of whole-body Model Predictive Control (whole-body MPC) continue to present challenges in generating a wide variety of movements using whole-body MPC for real humanoid robots. This
Externí odkaz:
http://arxiv.org/abs/2409.08488
Autor:
Yamamori, Satoshi, Morimoto, Jun
In this study, we propose the use of the phase-amplitude reduction method to construct an imitation learning framework. Imitating human movement trajectories is recognized as a promising strategy for generating a range of human-like robot movements.
Externí odkaz:
http://arxiv.org/abs/2406.03735
Autor:
Yagi, Satoshi, Tada, Mitsunori, Uchibe, Eiji, Kanoga, Suguru, Matsubara, Takamitsu, Morimoto, Jun
This study proposes an approach to human-to-humanoid teleoperation using GAN-based online motion retargeting, which obviates the need for the construction of pairwise datasets to identify the relationship between the human and the humanoid kinematics
Externí odkaz:
http://arxiv.org/abs/2406.00727
Autor:
Yamamori, Satoshi, Morimoto, Jun
In this study, we propose a multitask reinforcement learning algorithm for foundational policy acquisition to generate novel motor skills. Inspired by human sensorimotor adaptation mechanisms, we aim to train encoder-decoder networks that can be comm
Externí odkaz:
http://arxiv.org/abs/2308.16471
Object shaping by grinding is a crucial industrial process in which a rotating grinding belt removes material. Object-shape transition models are essential to achieving automation by robots; however, learning such a complex model that depends on proc
Externí odkaz:
http://arxiv.org/abs/2308.02150
Autor:
Yamanokuchi, Tomoya, Kwon, Yuhwan, Tsurumine, Yoshihisa, Uchibe, Eiji, Morimoto, Jun, Matsubara, Takamitsu
Many works have recently explored Sim-to-real transferable visual model predictive control (MPC). However, such works are limited to one-shot transfer, where real-world data must be collected once to perform the sim-to-real transfer, which remains a
Externí odkaz:
http://arxiv.org/abs/2207.01840
Autor:
Takai, Asuka, Fu, Qiushi, Doibata, Yuzuru, Lisi, Giuseppe, Tsuchiya, Toshiki, Mojtahedi, Keivan, Yoshioka, Toshinori, Kawato, Mitsuo, Morimoto, Jun, Santello, Marco
Collaboration frequently yields better results in decision making, learning, and haptic interactions than when these actions are performed individually. However, is collaboration always superior to solo actions, or do its benefits depend on whether c
Externí odkaz:
http://arxiv.org/abs/2205.06196
Currently, usual approaches for fast robot control are largely reliant on solving online optimal control problems. Such methods are known to be computationally intensive and sensitive to model accuracy. On the other hand, animals plan complex motor a
Externí odkaz:
http://arxiv.org/abs/1912.03535
Autor:
Furukawa, Jun-ichiro, Morimoto, Jun
In this study, we propose an optimal assistive control strategy that uses estimated user's movement intention as the terminal cost function. We estimate the movement intention by observing human user's joint angle, angluar velocity, and muscle activi
Externí odkaz:
http://arxiv.org/abs/1909.02288