4 citations · 4 across the 6 of their papers we have counts for
9 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…
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…
Body-Grounded Replanning for Physically Adaptive Manipulation
Namiko Saito, Hiroshi Kera
Manipulation requires not only reasoning about the external environment, but also about the robot's physical condition. A strategy may remain geometrically feasible while becoming…
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…
Replanning Human-Robot Collaborative Tasks with Vision-Language Models via Semantic and Physical Dual-Correction
Taichi Kato, Takuya Kiyokawa, Namiko Saito +1
Human-robot collaborative assembly requires robots to interpret ambiguous corrective instructions while producing physically executable motions. Vision-language models (VLMs) provi…
Explicit Contact Optimization in Whole-Body Contact-Rich Manipulation
Victor Leve, João Moura, Namiko Saito +2
Humans can exploit contacts anywhere on their body surface to manipulate large and heavy items, objects normally out of reach or multiple objects at once. However, such manipulatio…
Latent Object Characteristics Recognition with Visual to Haptic-Audio Cross-modal Transfer Learning
Namiko Saito, Joao Moura, Hiroki Uchida +1
Recognising the characteristics of objects while a robot handles them is crucial for adjusting motions that ensure stable and efficient interactions with containers. Ahead of reali…
Few-Shot Learning of Force-Based Motions From Demonstration Through Pre-training of Haptic Representation
Marina Y. Aoyama, João Moura, Namiko Saito +1
In many contact-rich tasks, force sensing plays an essential role in adapting the motion to the physical properties of the manipulated object. To enable robots to capture the under…