28 citations · 132 across the 19 of their papers we have counts for
23 papers
Dynamic Coloring and Graph Squares of Regular Graphs
Juan Gutierrez, Grover Ugarte
An -dynamic coloring of a graph is a proper vertex coloring in which every vertex sees at least distinct colors in its neighborhood. The minimum number…
Transporting Unsecured Stacked Payloads with a Quadrupedal Robot via Multi-Objective Reinforcement Learning
Nobuo Namura, Masayuki Hiromoto, Kento Uemura +2
Transporting unsecured payloads with legged robots over uneven terrain requires balancing locomotion performance and payload stability, since aggressive motion can destabilize the…
Toward Unified Robot Learning: Bridging Representation, Vision-Language-Action, and World Models
Shaunak A. Mehta, Ananya Hazarika, Haochen Zhang +5
For robots to operate reliably in real-world environments, they need to perceive their surroundings, act, and reason about the consequences of those actions. Rapid progress in the…
Co-training with Ego-centric Video and Demonstration for Robot Navigation Task
Shoya Kuno, Yumo Ouchi, Kanata Suzuki
Vision-language-action (VLA) models are promising for diverse robotic tasks, but their performance heavily depends on large-scale high-quality training data, whose collection on re…
How to Utilize Failure Demo Data?: Effective Data Selection for Imitation Learning Using Distribution Differences in Attention Mechanism
Kana Miyamoto, Kanata Suzuki, Tetsuya Ogata
Imitation learning for robotic tasks has relied primarily on policies trained only on successful demonstrations, although failures are unavoidable during human data collection. Man…
From Dialogue to Execution: Mixture-of-Agents Assisted Interactive Planning for Behavior Tree-Based Long-Horizon Robot Execution
Kanata Suzuki, Kazuki Hori, Haruka Miyoshi +2
Interactive task planning with large language models (LLMs) lets robots generate high-level action plans from natural language, but over long horizons it asks many questions, and t…
Compact Task-Aligned Imitation Learning for Laboratory Automation
Kanata Suzuki, Hanon Nakamura, Kana Miyamoto +1
Robotic laboratory automation has traditionally relied on carefully engineered motion pipelines and task-specific hardware interfaces, resulting in high design cost and limited fle…
Proprioception Enhances Vision Language Model in Generating Captions and Subtask Segmentations for Robot Task
Kanata Suzuki, Shota Shimizu, Tetsuya Ogata
From the perspective of future developments in robotics, it is crucial to verify whether foundation models trained exclusively on offline data, such as images and language, can und…
Sensorimotor Attention and Language-based Regressions in Shared Latent Variables for Integrating Robot Motion Learning and LLM
Kanata Suzuki, Tetsuya Ogata
In recent years, studies have been actively conducted on combining large language models (LLM) and robotics; however, most have not considered end-to-end feedback in the robot-moti…
Learning Multimodal Attention for Manipulating Deformable Objects with Changing States
Namiko Saito, Mayu Tatsumi, Ayuna Kubo +4
To support humans in their daily lives, robots are required to autonomously learn, adapt to objects and environments, and perform the appropriate actions. We tackled on the task of…
Interactively Robot Action Planning with Uncertainty Analysis and Active Questioning by Large Language Model
Kazuki Hori, Kanata Suzuki, Tetsuya Ogata
The application of the Large Language Model (LLM) to robot action planning has been actively studied. The instructions given to the LLM by natural language may include ambiguity an…
Deep Predictive Learning: Motion Learning Concept inspired by Cognitive Robotics
Kanata Suzuki, Hiroshi Ito, Tatsuro Yamada +2
Bridging the gap between motion models and reality is crucial by using limited data to deploy robots in the real world. Deep learning is expected to be generalized to diverse situa…
Learning Bidirectional Translation between Descriptions and Actions with Small Paired Data
Minori Toyoda, Kanata Suzuki, Yoshihiko Hayashi +1
This study achieved bidirectional translation between descriptions and actions using small paired data from different modalities. The ability to mutually generate descriptions and…
Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations
Akira Sakai, Taro Sunagawa, Spandan Madan +7
The training data distribution is often biased towards objects in certain orientations and illumination conditions. While humans have a remarkable capability of recognizing objects…
Annotation Cost Reduction of Stream-based Active Learning by Automated Weak Labeling using a Robot Arm
Kanata Suzuki, Taro Sunagawa, Tomotake Sasaki +1
Stream-based active learning (AL) is an efficient training data collection method, and it is used to reduce human annotation cost required in machine learning. However, it is diffi…
Embodying Pre-Trained Word Embeddings Through Robot Actions
Minori Toyoda, Kanata Suzuki, Hiroki Mori +2
We propose a promising neural network model with which to acquire a grounded representation of robot actions and the linguistic descriptions thereof. Properly responding to various…
In-air Knotting of Rope using Dual-Arm Robot based on Deep Learning
Kanata Suzuki, Momomi Kanamura, Yuki Suga +2
In this study, we report the successful execution of in-air knotting of rope using a dual-arm two-finger robot based on deep learning. Owing to its flexibility, the state of the ro…
Stable deep reinforcement learning method by predicting uncertainty in rewards as a subtask
Kanata Suzuki, Tetsuya Ogata
In recent years, a variety of tasks have been accomplished by deep reinforcement learning (DRL). However, when applying DRL to tasks in a real-world environment, designing an appro…
A Multi-task Learning Framework for Grasping-Position Detection and Few-Shot Classification
Yasuto Yokota, Kanata Suzuki, Yuzi Kanazawa +1
It is a big problem that a model of deep learning for a picking robot needs many labeled images. Operating costs of retraining a model becomes very expensive because the object sha…
Online Self-Supervised Learning for Object Picking: Detecting Optimum Grasping Position using a Metric Learning Approach
Kanata Suzuki, Yasuto Yokota, Yuzi Kanazawa +1
Self-supervised learning methods are attractive candidates for automatic object picking. However, the trial samples lack the complete ground truth because the observable parts of t…
HATSUKI : An anime character like robot figure platform with anime-style expressions and imitation learning based action generation
Pin-Chu Yang, Mohammed Al-Sada, Chang-Chieh Chiu +6
Japanese character figurines are popular and have pivot position in Otaku culture. Although numerous robots have been developed, less have focused on otaku-culture or on embodying…
Compensation for undefined behaviors during robot task execution by switching controllers depending on embedded dynamics in RNN
Kanata Suzuki, Hiroki Mori, Tetsuya Ogata
Robotic applications require both correct task performance and compensation for undefined behaviors. Although deep learning is a promising approach to perform complex tasks, the re…
Motion Switching with Sensory and Instruction Signals by designing Dynamical Systems using Deep Neural Network
Kanata Suzuki, Hiroki Mori, Tetsuya Ogata
To ensure that a robot is able to accomplish an extensive range of tasks, it is necessary to achieve a flexible combination of multiple behaviors. This is because the design of tas…