2 citations · 2 across the 10 of their papers we have counts for
10 papers
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…
DeReCo: Decoupling Representation and Coordination Learning for Object-Adaptive Decentralized Multi-Robot Cooperative Grasping and Lifting
Kazuki Shibata, Ryosuke Sota, Shandil Dhiresh Bosch +3
Generalizing decentralized multi-robot cooperative grasping and lifting across objects with diverse shapes and physical properties remains a fundamental challenge. Under decentrali…
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…
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…
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…
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…
Goal-Aware Generative Adversarial Imitation Learning from Imperfect Demonstration for Robotic Cloth Manipulation
Yoshihisa Tsurumine, Takamitsu Matsubara
Generative Adversarial Imitation Learning (GAIL) can learn policies without explicitly defining the reward function from demonstrations. GAIL has the potential to learn policies wi…
Randomized-to-Canonical Model Predictive Control for Real-world Visual Robotic Manipulation
Tomoya Yamanokuchi, Yuhwan Kwon, Yoshihisa Tsurumine +3
Many works have recently explored Sim-to-real transferable visual model predictive control (MPC). However, such works are limited to one-shot transfer, where real-world data must b…
Cyclic Policy Distillation: Sample-Efficient Sim-to-Real Reinforcement Learning with Domain Randomization
Yuki Kadokawa, Lingwei Zhu, Yoshihisa Tsurumine +1
Deep reinforcement learning with domain randomization learns a control policy in various simulations with randomized physical and sensor model parameters to become transferable to…
Binarized P-Network: Deep Reinforcement Learning of Robot Control from Raw Images on FPGA
Yuki Kadokawa, Yoshihisa Tsurumine, Takamitsu Matsubara
This paper explores a Deep Reinforcement Learning (DRL) approach for designing image-based control for edge robots to be implemented on Field Programmable Gate Arrays (FPGAs). Alth…