103 citations · 228 across the 24 of their papers we have counts for
10 papers · 1 filter
YUBI-Real2Sim: 3DGS Scene Verification for Reliable Policy Evaluation
Hiroki Nishizawa, Takehiko Ohkawa, Nicola Scianca +3
Evaluating VLA policies on real robots is costly, time-consuming, and difficult to reproduce, making real-to-sim simulation essential for scalable evaluation. While 3D Gaussian Spl…
PHASE: Compliance-Enabled Tactile Phase Retrieval for Few-Shot Insertion Learning
Jeremy Siburian, Cristian C. Beltran-Hernandez, Tatsuya Matsushima +3
Contact-rich assembly tasks such as peg-in-hole insertion remain difficult to learn from limited demonstrations. While retrieval-augmented imitation learning, which augments target…
Improving Cross-embodiment Transfer in Latent Action Models with Action-Similarity Supervision
Maxime Alvarez, Renzo Caballero, Tatsuya Matsushima +2
As generalist robot policies gain vision and language from web-scale pretraining, demonstrations remain costly to collect and tied to the robot that recorded them. Latent action mo…
MINERVA: How Small Can a Manipulation Policy Be and Still Solve LIBERO?
Kohei Sendai, Tatsuya Matsushima, Yusuke Iwasawa
Vision-language-action (VLA) models with billions of parameters now dominate the LIBERO manipulation benchmark, but the model capacity actually required by the benchmark remains un…
DREAM: Deployment-Time Demonstration Generation via Real-to-Sim for Scalable Policy Adaptation
Makoto Sato, Tatsuya Matsushima, Yutaka Matsuo +1
Vision-language-action (VLA) models have made strong progress in language-conditioned robot manipulation, but improving their performance in a new workspace still often requires ac…
Auditing Instruction-Trajectory Mismatches in Multimodal Robot Demonstrations
Simon Holk, Ryosuke Takanami, Tatsuya Matsushima +4
Robot demonstration datasets used to train vision-language-action policies can contain a subtle but harmful failure mode: trajectories that are behaviorally correct but paired with…
FlexLAM: Resolving the Bottleneck Trade-off in Latent Action Learning
Takanori Yoshimoto, Yang Hu, Naruya Kondo +1
Latent actions provide a compact interface between action-free video and downstream decision-making, yet existing Latent Action Models (LAMs) force every transition through a fixed…
YUBI: Yielding Universal Bidigital Interface for Bimanual Dexterous Manipulation at Scale
Takehiko Ohkawa, Jumpei Arima, Yuki Noguchi +16
We introduce Yielding Universal Bidigital Interface (YUBI), a finger-aligned gripper designed to enable intuitive, ergonomic, and scalable data collection for bimanual dexterous ma…
See Less, Specify More: Visual Evidence Budgets for Generalizable VLAs
Yueh-Hua Wu, Tatsuya Matsushima, Kei Ota
Generalization remains a central bottleneck for vision-language-action (VLA) models: under distractors, appearance shifts, and semantically similar tasks, the policy must often inf…
Continuous Reasoning for Vision-Language-Action
Yueh-Hua Wu, Tatsuya Matsushima, Kei Ota
Natural language is a powerful reasoning medium for language and vision-language models, but it is mismatched to the granularity of continuous control. Text and explicit subgoals o…