3 citations · 4 across the 5 of their papers we have counts for
3 papers · 1 filter
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…
Memory-based gaze prediction in deep imitation learning for robot manipulation
Heecheol Kim, Yoshiyuki Ohmura, Yasuo Kuniyoshi
Deep imitation learning is a promising approach that does not require hard-coded control rules in autonomous robot manipulation. The current applications of deep imitation learning…