2 citations · 3 across the 6 of their papers we have counts for
6 papers · 1 filter
Verify, Act, Distill: Turning Test-Time Compute into Test-Time Adaptation for Vision-Language-Action Models
Kaneyoshi Hiratsuka, Satoshi Yamamori, Jun Morimoto
Test-time compute (TTC), which scores multiple candidate actions at inference time, is a powerful way to improve the execution performance of vision-language-action (VLA) models, b…
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…
Object-Centric Mobile Manipulation through SAM2-Guided Perception and Imitation Learning
Wang Zhicheng, Satoshi Yagi, Satoshi Yamamori +1
Imitation learning for mobile manipulation is a key challenge in the field of robotic manipulation. However, current mobile manipulation frameworks typically decouple navigation an…
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…
Phase-Amplitude Reduction-Based Imitation Learning
Satoshi Yamamori, Jun Morimoto
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 pro…
Foundational Policy Acquisition via Multitask Learning for Motor Skill Generation
Satoshi Yamamori, Jun Morimoto
In this study, we propose a multitask reinforcement learning algorithm for foundational policy acquisition to generate novel motor skills. \textcolor{\hcolor}{Learning the rich rep…