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20212026
most citedPhysically Consistent Preferential Bayesian Optimization for Food Arrangement

2 citations · 2 across the 10 of their papers we have counts for

collaborators

10 papers

cs.RO2026

Robust Sim-to-Real Cloth Untangling through Reduced-Resolution Observations via Adaptive Force-Difference Quantization

Yoshihisa Tsurumine, Yuki Kadokawa, Kohei Hayashi +2

Robotic cloth untangling requires progressively disentangling fabric by adapting pulling actions to changing contact and tension conditions. Because large-scale real-world training…

cs.RO2026

DeReCo: Decoupling Representation and Coordination Learning for Object-Adaptive Decentralized Multi-Robot Cooperative Transport

Kazuki Shibata, Ryosuke Sota, Shandil Dhiresh Bosch +3

Generalizing decentralized multi-robot cooperative transport across objects with diverse shapes and physical properties remains a fundamental challenge. Under decentralized executi…

cs.RO2024

Cooperative Grasping and Transportation using Multi-agent Reinforcement Learning with Ternary Force Representation

Ing-Sheng Bernard-Tiong, Yoshihisa Tsurumine, Ryosuke Sota +2

Cooperative grasping and transportation require effective coordination to complete the task. This study focuses on the approach leveraging force-sensing feedback, where robots use…

cs.RO2024

Self-Supervised Learning of Grasping Arbitrary Objects On-the-Move

Takuya Kiyokawa, Eiki Nagata, Yoshihisa Tsurumine +2

Mobile grasping enhances manipulation efficiency by utilizing robots' mobility. This study aims to enable a commercial off-the-shelf robot for mobile grasping, requiring precise ti…

cs.RO2024

Robust Iterative Value Conversion: Deep Reinforcement Learning for Neurochip-driven Edge Robots

Yuki Kadokawa, Tomohito Kodera, Yoshihisa Tsurumine +2

A neurochip is a device that reproduces the signal processing mechanisms of brain neurons and calculates Spiking Neural Networks (SNNs) with low power consumption and at high speed…

eess.SY2022★ 2 cited

Physically Consistent Preferential Bayesian Optimization for Food Arrangement

Yuhwan Kwon, Yoshihisa Tsurumine, Takeshi Shimmura +2

This paper considers the problem of estimating a preferred food arrangement for users from interactive pairwise comparisons using Computer Graphics (CG)-based dish images. As a foo…