5 papers
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…
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…
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…