Hiroki Mori

发表

In-air Knotting of Rope using Dual-Arm Robot based on Deep Learning

Yuki Suga, Tetsuya Ogata, Hiroki Mori, 2021, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Efficient multitask learning with an embodied predictive model for door opening and entry with whole-body control

Kenjiro Yamamoto, Hiroshi Ito, H. Mori, 2022, Science Robotics.

Contact-Rich Manipulation of a Flexible Object based on Deep Predictive Learning using Vision and Tactility

Kenjiro Yamamoto, Hideyuki Ichiwara, Hiroshi Ito, 2021, 2022 International Conference on Robotics and Automation (ICRA).

Guided Visual Attention Model Based on Interactions Between Top-down and Bottom-up Prediction for Robot Pose Prediction

Hiroshi Ito, H. Mori, T. Ogata, 2022, IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society.

How to Select and Use Tools? : Active Perception of Target Objects Using Multimodal Deep Learning

Shigeki Sugano, Tetsuya Ogata, Hiroki Mori, 2021, IEEE Robotics and Automation Letters.

Visualization of Focal Cues for Visuomotor Coordination by Gradient-based Methods: A Recurrent Neural Network Shifts The Attention Depending on Task Requirements

Tetsuya Ogata, Kenjiro Yamamoto, Hiroki Mori, 2020, 2020 IEEE/SICE International Symposium on System Integration (SII).

Learning Multiple Sensorimotor Units to Complete Compound Tasks using an RNN with Multiple Attractors

Tetsuya Ogata, Hiroki Mori, Kei Kase, 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

A Peg-in-hole Task Strategy for Holes in Concrete

Hiroki Mori, André Yuji Yasutomi, Tetsuya Ogata, 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).

Multimodal Time Series Learning of Robots Based on Distributed and Integrated Modalities: Verification with a Simulator and Actual Robots

Kenjiro Yamamoto, Hideyuki Ichiwara, Hiroshi Ito, 2023, IEEE International Conference on Robotics and Automation.

Spatial Attention Point Network for Deep-learning-based Robust Autonomous Robot Motion Generation

T. Ogata, Kenjiro Yamamoto, Hideyuki Ichiwara, 2021, ArXiv.

Wiping 3D-objects using Deep Learning Model based on Image/Force/Joint Information

S. Sugano, T. Ogata, Danyang Wang, 2020, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Integrated Learning of Robot Motion and Sentences: Real-Time Prediction of Grasping Motion and Attention based on Language Instructions

Kenjiro Yamamoto, Hideyuki Ichiwara, Hiroshi Ito, 2022, 2022 International Conference on Robotics and Automation (ICRA).

Buttoning Task with a Dual-Arm Robot: An Exploratory Study on a Marker-based Algorithmic Method and Marker-less Machine Learning Methods

Kanata Suzuki, T. Ogata, Tomoki Ando, 2022, 2022 IEEE/SICE International Symposium on System Integration (SII).

Embodying Pre-Trained Word Embeddings Through Robot Actions

Yoshihiko Hayashi, Tetsuya Ogata, Hiroki Mori, 2021, IEEE Robotics and Automation Letters.

Compensation for Undefined Behaviors During Robot Task Execution by Switching Controllers Depending on Embedded Dynamics in RNN

Tetsuya Ogata, Hiroki Mori, Kanata Suzuki, 2021, IEEE Robotics and Automation Letters.

Motion Switching With Sensory and Instruction Signals by Designing Dynamical Systems Using Deep Neural Network

Tetsuya Ogata, Kanata Suzuki, Hiroki Mori, 2017, IEEE Robotics and Automation Letters.

Tactile-based curiosity maximizes tactile-rich object-oriented actions even without any extrinsic rewards

Tetsuya Ogata, Hiroki Mori, Masayuki Masuda, 2020, 2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob).

Online Motion Generation with Sensory Information and Instructions by Hierarchical RNN pdf

Tetsuya Ogata, Kanata Suzuki, Hiroki Mori, 2017, ArXiv.

Deep Active Visual Attention for Real-Time Robot Motion Generation: Emergence of Tool-Body Assimilation and Adaptive Tool-Use

Hiroshi Ito, H. Mori, T. Ogata, 2022, IEEE Robotics and Automation Letters.

Guided Visual Attention Model Based on Interactions Between Top-down and Bottom-up Information for Robot Pose Prediction

T. Ogata, Hyogo Hiruma, Hiroki Mori, 2022, ArXiv.

In-air Knotting of Rope using Dual-Arm Robot based on Deep Learning

Kanata Suzuki, T. Ogata, Y. Suga, 2021, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Modality Attention for Prediction-Based Robot Motion Generation: Improving Interpretability and Robustness of Using Multi-Modality

Kenjiro Yamamoto, Hideyuki Ichiwara, Hiroshi Ito, 2023, IEEE Robotics and Automation Letters.

Tool-Use Model to Reproduce the Goal Situations Considering Relationship Among Tools, Objects, Actions and Effects Using Multimodal Deep Neural Networks

Shigeki Sugano, Tetsuya Ogata, Shingo Murata, 2021, Frontiers in Robotics and AI.

Compensation for Undefined Behaviors During Robot Task Execution by Switching Controllers Depending on Embedded Dynamics in RNN

Kanata Suzuki, T. Ogata, Hiroki Mori, 2020, IEEE Robotics and Automation Letters.

Undefined-behavior guarantee by switching to model-based controller according to the embedded dynamics in Recurrent Neural Network pdf

Tetsuya Ogata, Kanata Suzuki, Hiroki Mori, 2020, ArXiv.

Evaluation of Generalization Performance of Visuo-Motor Learning by Analyzing Internal State Structured from Robot Motion

Hiroshi Ito, Tetsuya Ogata, Hiroki Mori, 2020, New Generation Computing.