6 papers · 1 filter
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…
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…
AnoleVLA: 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…
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…