51 citations · 90 across the 11 of their papers we have counts for
12 papers
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…
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…
RLDX-1 Technical Report
Dongyoung Kim, Huiwon Jang, Myungkyu Koo +65
While Vision-Language-Action models (VLAs) have shown remarkable progress toward human-like generalist robotic policies through the versatile intelligence (i.e. broad scene underst…
Multi-task real-robot data with gaze attention for dual-arm fine manipulation
Heecheol Kim, Yoshiyuki Ohmura, Yasuo Kuniyoshi
In the field of robotic manipulation, deep imitation learning is recognized as a promising approach for acquiring manipulation skills. Additionally, learning from diverse robot dat…
Goal-conditioned dual-action imitation learning for dexterous dual-arm robot manipulation
Heecheol Kim, Yoshiyuki Ohmura, Yasuo Kuniyoshi
Long-horizon dexterous robot manipulation of deformable objects, such as banana peeling, is a problematic task because of the difficulties in object modeling and a lack of knowledg…
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…
Training Robots without Robots: Deep Imitation Learning for Master-to-Robot Policy Transfer
Heecheol Kim, Yoshiyuki Ohmura, Akihiko Nagakubo +1
Deep imitation learning is promising for robot manipulation because it only requires demonstration samples. In this study, deep imitation learning is applied to tasks that require…
Transformer-based deep imitation learning for dual-arm robot manipulation
Heecheol Kim, Yoshiyuki Ohmura, Yasuo Kuniyoshi
Deep imitation learning is promising for solving dexterous manipulation tasks because it does not require an environment model and pre-programmed robot behavior. However, its appli…
Gaze-based dual resolution deep imitation learning for high-precision dexterous robot manipulation
Heecheol Kim, Yoshiyuki Ohmura, Yasuo Kuniyoshi
A high-precision manipulation task, such as needle threading, is challenging. Physiological studies have proposed connecting low-resolution peripheral vision and fast movement to t…
Macro Action Reinforcement Learning with Sequence Disentanglement using Variational Autoencoder
Heecheol Kim, Masanori Yamada, Kosuke Miyoshi +1
One problem in the application of reinforcement learning to real-world problems is the curse of dimensionality on the action space. Macro actions, a sequence of primitive actions,…
FAVAE: Sequence Disentanglement using Information Bottleneck Principle
Masanori Yamada, Heecheol Kim, Kosuke Miyoshi +1
We propose the factorized action variational autoencoder (FAVAE), a state-of-the-art generative model for learning disentangled and interpretable representations from sequential da…