Publications
Papers published before the name change appear under Tomoya Yamanokuchi. Also listed on Google Scholar.
Under Review
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Under reviewVoxelDiffusionCut: Non-destructive Internal-part Extraction via Iterative Cutting and Structure Estimation -
Under reviewViSA: Visited-State Augmentation for Generalized Goal-Space Contrastive Reinforcement Learning
International Journals
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RAS 2026DISF: Disentangled Iterative Surface Fitting for Contact-stable Grasp Planning with Grasp Pose Alignment to the Object Center of MassIn this work, we address the limitation of surface fitting-based grasp planning algorithm, which primarily focuses on geometric alignment between the gripper and object surface while overlooking the stability of contact point distribution, often resulting in unstable grasps due to inadequate contact configurations. To overcome this limitation, we propose a novel surface fitting algorithm that integrates contact stability while preserving geometric compatibility. Inspired by human grasping behavior, our method disentangles the grasp pose optimization into three sequential steps: (1) rotation optimization to align contact normals, (2) translation refinement to improve the alignment between the gripper frame origin and the object Center of Mass (CoM), and (3) gripper aperture adjustment to optimize contact point distribution. We validate our approach in simulation across 15 objects under both Known-shape (with clean CAD-derived dataset) and Observed-shape (with YCB object dataset) settings, including cross-platform grasp execution on three robot–gripper platforms. We further validate the method in real-world grasp experiments on a UR3e robot. Overall, DISF reduces CoM misalignment while maintaining geometric compatibility, translating into higher grasp success in both simulation and real-world execution compared to baselines.
@article{yamanokuchi2026disf_ras, author = {Yamanokuchi, Tomoya and Bacchin, Alberto and Olivastri, Emilio and Arifuku, Ryotaro and Matsubara, Takamitsu and Menegatti, Emanuele}, title = {DISF: Disentangled Iterative Surface Fitting for Contact-stable Grasp Planning with Grasp Pose Alignment to the Object Center of Mass}, journal = {Robotics and Autonomous Systems}, year = {2026}, url = {https://www.sciencedirect.com/science/article/abs/pii/S0921889026004082}, } -
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Frontiers 2025Weber-Fechner Law in Temporal Difference Learning Derived from Control as Inference -
RA-L 2022Randomized-to-Canonical Model Predictive Control for Real-world Visual Robotic ManipulationMany 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 be collected once to perform the sim-to-real transfer, which remains a significant human effort of transferring the models learned in simulations to new domains in the real world. To alleviate these problems, we first propose a novel model-learning framework called Kalman Randomized-to-Canonical Model (KRC-model). This framework is capable of extracting task-relevant intrinsic features and their dynamics from randomized images. We then propose Kalman Randomized-to-Canonical Model Predictive Control (KRC-MPC) as a zero-shot sim-to-real transferable visual MPC using KRC-model. The effectiveness of our method is evaluated through valve rotation tasks by a robot hand in both simulation and the real world, and the block mating tasks in simulation. The experimental results show that KRC-MPC can be applied to various real domains and tasks in a zero-shot manner.
@article{yamanokuchi2022rcmpc, author = {Yamanokuchi, Tomoya and Kwon, Yuhwan and Tsurumine, Yoshihisa and Uchibe, Eiji and Morimoto, Jun and Matsubara, Takamitsu}, title = {Randomized-to-Canonical Model Predictive Control for Real-world Visual Robotic Manipulation}, journal = {IEEE Robotics and Automation Letters}, volume = {7}, pages = {8964--8971}, year = {2022}, url = {https://ieeexplore.ieee.org/document/9817636}, }
International Conferences
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IAS-19 2025Disentangled Iterative Surface Fitting for Contact-stable Grasp PlanningIn this work, we address the limitation of surface fitting-based grasp planning algorithm, which primarily focuses on geometric alignment between the gripper and object surface while overlooking the stability of contact point distribution, often resulting in unstable grasps due to inadequate contact configurations. To overcome this limitation, we propose a novel surface fitting algorithm that integrates contact stability while preserving geometric compatibility. Inspired by human grasping behavior, our method disentangles the grasp pose optimization into three sequential steps: (1) rotation optimization to align contact normals, (2) translation refinement to improve Center of Mass (CoM) alignment, and (3) gripper aperture adjustment to optimize contact point distribution. We validate our approach through simulations on ten YCB dataset objects, demonstrating an 80% improvement in grasp success over conventional surface fitting methods that disregard contact stability.
@inproceedings{yamanokuchi2025disf, author = {Yamanokuchi, Tomoya and Bacchin, Alberto and Olivastri, Emilio and Matsubara, Takamitsu and Menegatti, Emanuele}, title = {Disentangled Iterative Surface Fitting for Contact-stable Grasp Planning}, booktitle = {International Conference on Intelligent Autonomous Systems (IAS-19)}, year = {2025}, eprint = {2502.11535}, archivePrefix = {arXiv}, } -
IROS 2024Domain Randomization-free Sim-to-Real: An Attention-Augmented Memory Approach for Robotic Tasks -
IROS 2022Randomized-to-Canonical Model Predictive Control for Real-world Visual Robotic Manipulation -
SICE 2018Dimensionality Reduction Method for Gaussian Process Regression and Its Application to Object Recognition Tasks
Domestic Conferences (in Japanese)
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RSJ 2025到達状態拡張による対照強化学習の汎化 -
ROBOMECH 2024Sim-to-Real 方策転移のためのダイナミクスランダム化対照強化学習 -
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四国支部 2017ガウス過程回帰のための次元削減法
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SICE四国 2016サポートベクターマシンを用いたアクセル操作の予測