2 citations · 2 across the 15 of their papers we have counts for
15 papers
LILAC: Language-Conditioned Object-Centric Optical Flow for Open-Loop Trajectory Generation
Motonari Kambara, Koki Seno, Tomoya Kaichi +2
We address language-conditioned robotic manipulation using flow-based trajectory generation, which enables training on human and web videos of object manipulation and requires only…
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…
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…
Pre-Manipulation Alignment Prediction with Parallel Deep State-Space and Transformer Models
Motonari Kambara, Komei Sugiura
In this work, we address the problem of predicting the future success of open-vocabulary object manipulation tasks. Conventional approaches typically determine success or failure a…
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…
Future Success Prediction in Open-Vocabulary Object Manipulation Tasks Based on End-Effector Trajectories
Motonari Kambara, Komei Sugiura
This study addresses a task designed to predict the future success or failure of open-vocabulary object manipulation. In this task, the model is required to make predictions based…
Task Success Prediction for Open-Vocabulary Manipulation Based on Multi-Level Aligned Representations
Miyu Goko, Motonari Kambara, Daichi Saito +2
In this study, we consider the problem of predicting task success for open-vocabulary manipulation by a manipulator, based on instruction sentences and egocentric images before and…
Nearest Neighbor Future Captioning: Generating Descriptions for Possible Collisions in Object Placement Tasks
Takumi Komatsu, Motonari Kambara, Shumpei Hatanaka +5
Domestic service robots (DSRs) that support people in everyday environments have been widely investigated. However, their ability to predict and describe future risks resulting fro…
Object Segmentation from Open-Vocabulary Manipulation Instructions Based on Optimal Transport Polygon Matching with Multimodal Foundation Models
Takayuki Nishimura, Katsuyuki Kuyo, Motonari Kambara +1
We consider the task of generating segmentation masks for the target object from an object manipulation instruction, which allows users to give open vocabulary instructions to dome…
Learning-To-Rank Approach for Identifying Everyday Objects Using a Physical-World Search Engine
Kanta Kaneda, Shunya Nagashima, Ryosuke Korekata +2
Domestic service robots offer a solution to the increasing demand for daily care and support. A human-in-the-loop approach that combines automation and operator intervention is con…
DialMAT: Dialogue-Enabled Transformer with Moment-Based Adversarial Training
Kanta Kaneda, Ryosuke Korekata, Yuiga Wada +7
This paper focuses on the DialFRED task, which is the task of embodied instruction following in a setting where an agent can actively ask questions about the task. To address this…
Fully Automated Task Management for Generation, Execution, and Evaluation: A Framework for Fetch-and-Carry Tasks with Natural Language Instructions in Continuous Space
Motonari Kambara, Komei Sugiura
This paper aims to develop a framework that enables a robot to execute tasks based on visual information, in response to natural language instructions for Fetch-and-Carry with Obje…
Switching Head-Tail Funnel UNITER for Dual Referring Expression Comprehension with Fetch-and-Carry Tasks
Ryosuke Korekata, Motonari Kambara, Yu Yoshida +4
This paper describes a domestic service robot (DSR) that fetches everyday objects and carries them to specified destinations according to free-form natural language instructions. G…
Relational Future Captioning Model for Explaining Likely Collisions in Daily Tasks
Motonari Kambara, Komei Sugiura
Domestic service robots that support daily tasks are a promising solution for elderly or disabled people. It is crucial for domestic service robots to explain the collision risk be…
Case Relation Transformer: A Crossmodal Language Generation Model for Fetching Instructions
Motonari Kambara, Komei Sugiura
There have been many studies in robotics to improve the communication skills of domestic service robots. Most studies, however, have not fully benefited from recent advances in dee…