Namiko SAITO -Robotics & AI

5. Namiko Saito, Kiyoshi Yoshinaka, Shigeki Sugano and Ryosuke Tsumura, ``Autonomous scanning motion generation adapted to individual differences in abdominal shape for robotic fetal ultrasound,'' Advanced Robotics, 38:3, pp.182--191, 2024, doi: 10.1080/01691864.2024.2315058

4. Namiko Saito, Takumi Shimizu, Tetsuya Ogata and Shigeki Sugano, ``Utilization of Image/Force/Tactile Sensor Data for Object-Shape-Oriented Manipulation: Wiping Objects With Turning Back Motions and Occlusion,'' in IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 968-975, 2022, doi: 10.1109/LRA.2021.3136657. (IF: 3.741)

Presented in 2022 International Conference on Robotics and Automation (ICRA 2022), 2022. (in Philadelphia, the USA) Travel Award [[video]]

3. Tamon Miyake, Tomoyuki Suzuki, Satoshi Funabashi, Namiko Saito, Mitsuhiro Kamezaki, Takahiro Shoda, Tsutomu Saigo and Shigeki Sugano, ``Bayesian Estimation of Model Parameters of Equivalent Circuit Model for Detecting Degradation Parts of Lithium-Ion Battery,'' in IEEE Access, vol. 9, pp. 159699-159713, 2021, doi: 10.1109/ACCESS.2021.3131190.

2. Namiko Saito, Tetsuya Ogata, Hiroki Mori, Shingo Murata and Shigeki Sugano, ``Tool-use Model to Reproduce the Goal Situations Considering Relationship among Tools, Objects, Actions and Effects Using Multimodal Deep Neural Networks,'' Frontiers in Robotics and AI, vol. 8, September, 2021. (IF: 4.33) DOI=10.3389/frobt.2021.748716, ISSN=2296-9144  [[video]]

1. Namiko Saito, Tetsuya Ogata, Satoshi Funabashi, Hiroki Mori and Shigeki Sugano, ``How to select and use tools? : Active Perception of Target Objects Using Multimodal Deep Learning,'' In the IEEE Robotics and Automation Letters (RA-L), vol. 6, no. 2, pp. 2517-2524, April 2021, doi: 10.1109/LRA.2021.3062004. (IF: 3.608), (arxiv.org/abs/2106.02445)

Presented in 2021 International Conference on Robotics and Automation (ICRA 2021), 2021. (in Xi'an, China (virtual-online), Acceptance rate 43.59%, Best Paper Award of Cognitive Robotics) [[video]]

7. Marina Y. Aoyama, Joao Moura, Namiko Saito and Sethu Vijayakumar, Few-Shot Learning of Force-Based Motions From Demonstration Through Pre-training of Haptic Representation, In Proceedings of 2023 IEEE International Conference on Robotics and Automation (ICRA 2024), 2024. (in Yokohama, Japan) (arXiv/abs/2309.04640)

6. Namiko Saito, Joao Moura, Tetsuya Ogata, Marina Y. Aoyama, Shingo Murata, Shigeki Sugano, and Sethu Vijayakumar, ``Structured Motion Generation with Predictive Learning: Proposing Subgoal for Long-Horizon Manipulation,'' In Proceedings of 2023 IEEE International Conference on Robotics and Automation (ICRA 2023), pp. 9566-9572, 2023. (in London, U.K., acceptance rate: 43.04%,) doi: 10.1109/ICRA48891.2023.10161046. [[video]]

5. Namiko Saito, Dangyang Wang, Tetsuya Ogata, Hiroki Mori and Shigeki Sugano, ``Wiping 3D-objects using Deep Learning Model based on Image/Force/Joint Information,'' In Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS 2020), pp. 10152-10157, 2020. (in Las Vegas, the USA (online), Acceptance rate 47%) doi: 10.1109/IROS45743.2020.9341275. [[video]]

4. Peizhi Zhang, Namiko Saito, Hiroki Shigemune, Shigeki Sugano, ``Development of a Lightweight Deformable Surface Mechanism (DSM) by Applying Shape-Memory Alloy (SMA) and the Sponge for Handling Objects,'' In Proceedings of the IEEE International Conference on Systems,  Man, and Cybernetics (SMC 2020), pp. 1406-1411, 2020.  (in Tronto, Canada (online)) doi: 10.1109/SMC42975.2020.9283459

3. Namiko Saito, Nguyen Ba Dai, Tetsuya Ogata, Hiroki Mori and Shigeki Sugano,``Realtime Liquid Pouring Motion Generation: End-to-End Sensorimotor Coordination for Unknown Liquid Dynamics Trained with Deep Neural Networks,'' In Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO 2019), pp. 1077-1082, 2019.  (in Dali, China, Acceptance rate: 54%) doi: 10.1109/ROBIO49542.2019.8961718. [[video]]

2. Namiko Saito, Kitae Kim, Shingo Murata, Tetsuya Ogata and Shigeki Sugano, ``Tool-use Model Considering Tool Selection by a Robot using Deep Learning,'' In Proceedings of the 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids 2018), pp. 270-276, 2018.  (in Beijing, China, Acceptance rate for oral presentation was 19.5%) doi: 10.1109/HUMANOIDS.2018.8625048.  [[video]]

1. Namiko Saito, Kitae Kim, Shingo Murata, Tetsuya Ogata and Shigeki Sugano, ``Detecting Features of Tools, Objects, and Actions from Effects in a Robot using Deep Learning,'' In Proceeding of IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-Epirob 2018), pp. 1-6, 2018.  (in Tokyo, Japan) doi: 10.1109/DEVLRN.2018.8761029. (arxiv.org/abs/1809.08613)

Namiko Saito, Joao Moura, Hiroki Uchida, and Sethu Vijayakumar, ``Latent Object Characteristics Recognition with Visual to Haptic-Audio Cross-modal Transfer Learning'', Mar 2024. arxiv.org/abs/2403.10689

Namiko Saito, Mayu Hiramoto, Ayuna Kubo, Kanata Suzuki, Hiroshi Ito, Shigeki Sugano, and Tetsuya Ogata, ``Realtime Motion Generation with Active Perception Using Attention Mechanism for Cooking Robot,'' Sep 2023. arxiv.org/abs/2309.14837