103 citations · 228 across the 24 of their papers we have counts for
29 papers
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…
AIRoA MoMa Dataset: A Large-Scale Hierarchical Dataset for Mobile Manipulation
Ryosuke Takanami, Petr Khrapchenkov, Shu Morikuni +32
As robots transition from controlled settings to unstructured human environments, building generalist agents that can reliably follow natural language instructions remains a centra…
Leave No Observation Behind: Real-time Correction for VLA Action Chunks
Kohei Sendai, Maxime Alvarez, Tatsuya Matsushima +2
To improve efficiency and temporal coherence, Vision-Language-Action (VLA) models often predict action chunks; however, this action chunking harms reactivity under inference delay…
Retrieve-Augmented Generation for Speeding up Diffusion Policy without Additional Training
Sodtavilan Odonchimed, Tatsuya Matsushima, Simon Holk +2
Diffusion Policies (DPs) have attracted attention for their ability to achieve significant accuracy improvements in various imitation learning tasks. However, DPs depend on Diffusi…
SPARK: Graph-Based Online Semantic Integration System for Robot Task Planning
Mimo Shirasaka, Yuya Ikeda, Tatsuya Matsushima +2
The ability to update information acquired through various means online during task execution is crucial for a general-purpose service robot. This information includes geometric an…
A Comprehensive Survey on Physical Risk Control in the Era of Foundation Model-enabled Robotics
Takeshi Kojima, Yaonan Zhu, Yusuke Iwasawa +8
Recent Foundation Model-enabled robotics (FMRs) display greatly improved general-purpose skills, enabling more adaptable automation than conventional robotics. Their ability to han…
Towards Embodiment Scaling Laws in Robot Locomotion
Bo Ai, Liu Dai, Nico Bohlinger +7
Cross-embodiment generalization underpins the vision of building generalist embodied agents for any robot, yet its enabling factors remain poorly understood. We investigate embodim…
RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
Charles Xu, Qiyang Li, Jianlan Luo +1
Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, th…
Real-World Robot Applications of Foundation Models: A Review
Kento Kawaharazuka, Tatsuya Matsushima, Andrew Gambardella +3
Recent developments in foundation models, like Large Language Models (LLMs) and Vision-Language Models (VLMs), trained on extensive data, facilitate flexible application across dif…
TRAIL Team Description Paper for RoboCup@Home 2023
Chikaha Tsuji, Dai Komukai, Mimo Shirasaka +13
Our team, TRAIL, consists of AI/ML laboratory members from The University of Tokyo. We leverage our extensive research experience in state-of-the-art machine learning to build gene…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
Self-Recovery Prompting: Promptable General Purpose Service Robot System with Foundation Models and Self-Recovery
Mimo Shirasaka, Tatsuya Matsushima, Soshi Tsunashima +8
A general-purpose service robot (GPSR), which can execute diverse tasks in various environments, requires a system with high generalizability and adaptability to tasks and environm…
GenDOM: Generalizable One-shot Deformable Object Manipulation with Parameter-Aware Policy
So Kuroki, Jiaxian Guo, Tatsuya Matsushima +7
Due to the inherent uncertainty in their deformability during motion, previous methods in deformable object manipulation, such as rope and cloth, often required hundreds of real-wo…
GenORM: Generalizable One-shot Rope Manipulation with Parameter-Aware Policy
So Kuroki, Jiaxian Guo, Tatsuya Matsushima +7
Due to the inherent uncertainty in their deformability during motion, previous methods in rope manipulation often require hundreds of real-world demonstrations to train a manipulat…
Collective Intelligence for 2D Push Manipulations with Mobile Robots
So Kuroki, Tatsuya Matsushima, Jumpei Arima +4
While natural systems often present collective intelligence that allows them to self-organize and adapt to changes, the equivalent is missing in most artificial systems. We explore…