3 papers
cs.RO2026
Actuator Reality Shaping for Zero-Shot Sim-to-Real Robot Learning
Satoshi Yamamori, Koji Ishihara, Kenjiro Minamikawa +4
Sim-to-real transfer in robot learning is often limited by discrepancies between the ideal actuator dynamics assumed during policy training and the nonlinear, hardware-dependent be…
cs.RO2024
Goal-Conditioned Terminal Value Estimation for Real-time and Multi-task Model Predictive Control
Mitsuki Morita, Satoshi Yamamori, Satoshi Yagi +2
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 t…
cs.RO2014
Efficient Reuse of Previous Experiences to Improve Policies in Real Environment
Norikazu Sugimoto, Voot Tangkaratt, Thijs Wensveen +3
In this study, we show that a movement policy can be improved efficiently using the previous experiences of a real robot. Reinforcement Learning (RL) is becoming a popular approach…