48 citations · 54 across the 14 of their papers we have counts for
4 papers · 1 filter
DiVeR: Decision-Critical Verifier Learning for VLA Test-Time Scaling
Seongheon Park, Heecheol Kim, Shulin Tian +5
Scaling robot data and model capacity has improved Vision-Language-Action (VLA) policies, but further progress is constrained by the high cost of robotic data. Verifier-guided test…
VLaRL: Augmenting Vision-Language-Action Models with Simulation-Trained Latent-Conditioned Residual RL
Namiko Saito, Kinam Kim, Heecheol Kim +2
Vision-language-action (VLA) models provide broad, instruction-conditioned manipulation behaviors, but their physical execution can remain imprecise during contact-rich interaction…
ConCent: Contact-Centric Real-to-Sim-to-Real Learning from One Demonstration
Heecheol Kim, Namiko Saito, Katsushi Ikeuchi +1
Sim-to-real policy transfer -- deploying policies trained in simulation in the real world -- is a promising paradigm for scaling robot manipulation without large-scale real-world d…
Object-Centric Residual RL for Zero-Shot Sim-to-Real VLA Enhancement
Kinam Kim, Namiko Saito, Heecheol Kim +3
Vision-Language-Action (VLA) models can generalize across diverse manipulation tasks, but their imitation-learning-based policies remain brittle in precise physical interactions du…