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Yukiyasu Domae

16 accepted papers

2026

Prompt2Craft: Generating Functional Craft Assemblies With LLMs

RA-L 2026

The Craft Assembly Task - a robotic assembly task inspired by handmade crafts - poses unique challenges relative to a traditional object assembly task.It involves open-ended decisions that are difficult to automate, with previous work relying on prior assumptions or expert knowledge, limiting scalab

Cited by 0SourceScholar
2025

Robust Instant Policy: Leveraging Student's t-Regression Model for Robust In-context Imitation Learning of Robot Manipulation

IROS 2025

Imitation learning (IL) aims to enable robots to perform tasks autonomously by observing a few human demonstrations. Recently, a variant of IL, called In-Context IL, utilized off-the-shelf large language models (LLMs) as instant policies that understand the context from a few given demonstrations to

Cited by 1SourceScholar
2024

Component Selection for Craft Assembly Tasks

RA-L 2024

Inspired by traditional handmade crafts, where a person improvises assemblies based on the available objects, we formally introduce the Craft Assembly Task. It is a robotic assembly task that involves building an accurate representation of a given target object using the available objects, which do

Cited by 1SourceScholar
2024

NeuralLabeling: A versatile toolset for labeling vision datasets using Neural Radiance Fields

IROS 2024poster

We present NeuralLabeling, a labeling approach and toolset for annotating 3D scenes using either bounding boxes or meshes and generating segmentation masks, affordance maps, 2D bounding boxes, 3D bounding boxes, 6DOF object poses, depth maps, and object meshes. NeuralLabeling uses Neural Radiance Fi…

Cited by 3SourcecodeScholar
2024

PEGASUS: Physically Enhanced Gaussian Splatting Simulation System for 6DoF Object Pose Dataset Generation

IROS 2024poster

We introduce Physically Enhanced Gaussian Splatting Simulation System (PEGASUS) for 6DoF object pose dataset generation, a versatile dataset generator based on 3D Gaussian Splatting. Environment and object representations can be easily obtained using commodity cameras to reconstruct with Gaussian Sp…

Cited by 8SourcecodeScholar
2023

Force Map: Learning to Predict Contact Force Distribution from Vision

IROS 2023poster

When humans see a scene, they can roughly imagine the forces applied to objects based on their expe-rience and use them to handle the objects properly. This paper considers transferring this “force-visualization” ability to robots. We hypothesize that a rough force distribution (named “force map”) c…

Cited by 4SourceScholar
2023

Learning Depth Completion of Transparent Objects using Augmented Unpaired Data

ICRA 2023poster

We propose a technique for depth completion of transparent objects using augmented data captured directly from real environments with complicated geometry. Using cyclic adversarial learning we train translators to convert between painted versions of the objects and their real transparent counterpart…

Cited by 7SourcecodeScholar
2023

Learning Efficient Policies for Picking Entangled Wire Harnesses: An Approach to Industrial Bin Picking

RA-L 2023

Wire harnesses are essential connecting components in manufacturing industry but are challenging to be automated in industrial tasks such as bin picking. They are long, flexible and tend to get entangled when randomly placed in a bin. This makes it difficult for the robot to grasp a single one in de

Cited by 27SourcecodeScholar
2023

Learning to Dexterously Pick or Separate Tangled-Prone Objects for Industrial Bin Picking

RA-L 2023

Industrial bin picking for tangled-prone objects requires the robot to either pick up untangled objects or perform separation manipulation when the bin contains no isolated objects. The robot must be able to flexibly perform appropriate actions based on the current observation. It is challenging due

Cited by 5SourceScholar
2022

Point Cloud Pre-Training With Natural 3D Structures

CVPR 2022poster

The construction of 3D point cloud datasets requires a great deal of human effort. Therefore, constructing a largescale 3D point clouds dataset is difficult. In order to remedy this issue, we propose a newly developed point cloud fractal database (PC-FractalDB), which is a novel family of formula-dr…

Cited by 46PDFcodeScholar
2022

Use of Action Label in Deep Predictive Learning for Robot Manipulation

IROS 2022poster

Various forms of human knowledge can be explicitly used to enhance deep robot learning from demonstrations. Annotation of subtasks from task segmentation is one type of human symbolism and knowledge. Annotated subtasks can be referred to as action labels, which are more primitive symbols that can be…

Cited by 4SourceScholar
2020

Planning an Efficient and Robust Base Sequence for a Mobile Manipulator Performing Multiple Pick-and-place Tasks

ICRA 2020poster

In this paper, we address efficiently and robustly collecting objects stored in different trays using a mobile manipulator. A resolution complete method, based on precomputed reachability database, is proposed to explore collision-free inverse kinematics (IK) solutions and then a resolution complete…

Cited by 47SourceScholar
2020

Robotic General Parts Feeder: Bin-picking, Regrasping, and Kitting

ICRA 2020poster

The automatic parts feeding of multiple objects is an unsolved problem in the manufacturing industry. In this paper, we tackle the problem by proposing a multi-robot system. The system comprises three sub-components which perform bin-picking, regrasping, and kitting. The three subcomponents divide a…

Cited by 19SourceScholar
2019

Fast and Precise Detection of Object Grasping Positions with Eigenvalue Templates

ICRA 2019poster

Fast Graspability Evaluation (FGE) has been proposed as a method for detecting grasping positions on objects and is now being used for industrial robots. FGE uses convolution of hand templates with regions on the target object to estimate the optimum grasping posture. However, the hand opening width…

Cited by 5SourceScholar
2019

Learning Based Robotic Bin-picking for Potentially Tangled Objects

IROS 2019poster

In this research, we tackle the challenge of picking only one object from a randomly stacked pile where the objects can potentially be tangled. No solution has been proposed to solve this challenge due to the complexity of picking one and only one object from the bin of tangled objects. Therefore, w…

Cited by 56SourceScholar