13 papers
NarrativeFlow: Flow-Based Vision-Language-Action Model Using Robot Velocity Fields
Shota Kobayashi, Koki Seno, Daichi Yashima +1
We focus on language-conditioned flow-based manipulation, where robot flows (robot velocity fields) serve as embodiment-agnostic, motion-centric representations for leveraging data…
Rigel: Self-Distilled Score Adaptation for Image and Video Captioning Evaluation
Shuitsu Koyama, Kazuki Matsuda, Yuiga Wada +3
Automatic evaluation of image and video captioning is essential for benchmarking multimodal systems, although standard evaluation metrics show limited alignment with human judgment…
Flow as Flow: Modeling Robot Velocity Fields as Probability Velocity Fields for Flow-Based Object Manipulation
Koki Seno, Daichi Yashima, Yusuke Takagi +2
Cross-embodiment data have become central to training robotic foundation models. To leverage such heterogeneous data, we focus on flow-based object manipulation, where robot flows…
ELSA: Acoustic Event-Level Semantic Alignment for Fine-Grained Reference-Free Text-to-Audio Evaluation
Shuntaro Suzuki, Kento Tokura, Daichi Yashima +3
Text-to-audio (TTA) generation, synthesizing audio from natural language, has been widely studied for its ability to capture precise user intent. To effectively advance TTA models,…
MLLM-as-a-Judge Exhibits Model Preference Bias
Shuitsu Koyama, Yuiga Wada, Daichi Yashima +1
Automatic evaluation using multimodal large language models (MLLMs), commonly referred to as MLLM-as-a-Judge, has been widely used to measure model performance. If such MLLM-as-a-J…
ABMAMBA: Multimodal Large Language Model with Aligned Hierarchical Bidirectional Scan for Efficient Video Captioning
Daichi Yashima, Shuhei Kurita, Yusuke Oda +3
In this study, we focus on video captioning by fully open multimodal large language models (MLLMs). The comprehension of visual sequences is challenging because of their intricate…
HiFlow: Tokenization-Free Scale-Wise Autoregressive Policy Learning via Flow Matching
Daichi Yashima, Koki Seno, Shuhei Kurita +2
Coarse-to-fine autoregressive modeling has recently shown strong promise for visuomotor policy learning, combining the inference efficiency of autoregressive methods with the globa…
AnolisVLA: Lightweight Vision-Language-Action Model with Deep State Space Models for Mobile Manipulation
Yusuke Takagi, Motonari Kambara, Daichi Yashima +3
In this study, we address the problem of language-guided robotic manipulation, where a robot is required to manipulate a wide range of objects based on visual observations and natu…
NaiLIA: Multimodal Nail Design Retrieval Based on Dense Intent Descriptions and Palette Queries
Kanon Amemiya, Daichi Yashima, Kei Katsumata +4
We focus on the task of retrieving nail design images based on dense intent descriptions, which represent multi-layered user intent for nail designs. This is challenging because su…
ReMoRa: Multimodal Large Language Model based on Refined Motion Representation for Long-Video Understanding
Daichi Yashima, Shuhei Kurita, Yusuke Oda +1
While multimodal large language models (MLLMs) have shown remarkable success across a wide range of tasks, long-form video understanding remains a significant challenge. In this st…
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…
Mobile Manipulation Instruction Generation from Multiple Images with Automatic Metric Enhancement
Kei Katsumata, Motonari Kambara, Daichi Yashima +2
We consider the problem of generating free-form mobile manipulation instructions based on a target object image and receptacle image. Conventional image captioning models are not a…
Open-Vocabulary Mobile Manipulation Based on Double Relaxed Contrastive Learning with Dense Labeling
Daichi Yashima, Ryosuke Korekata, Komei Sugiura
Growing labor shortages are increasing the demand for domestic service robots (DSRs) to assist in various settings. In this study, we develop a DSR that transports everyday objects…