Publication

Journal paper†

• Akihiko Yamaguchi, and Christopher G. Atkeson:
  Tactile Behaviors with the Vision-Based Tactile Sensor FingerVision,
  International Journal of Humanoid Robotics, Vol.16, No.03, 2019. [final-draft] [World Scientific]
• Akihiko Yamaguchi, and Christopher G. Atkeson:
  Recent progress in tactile sensing and sensors for robotic manipulation: can we turn tactile sensing into vision?,
  Advanced Robotics, Vol.33, No.14, pp.661-673, 2019.
  DOI:10.1080/01691864.2019.1632222 [final-draft] [Taylor & Francis]
• Akihiko Yamaguchi, Jun Takamatsu, Atsutoshi Ikeda, Masahiro Yoshikawa, Takamitsu Matsubara, Masayuki Kanbara, and Tsukasa Ogasawara:
  Graduate Curriculum of Robot Education Centered on Project based Learning,
  Journal of the Robotics Society of Japan, Vol.34, No.9, pp.615-622, 2016. (in Japanese) [final-draft+col-figs] [JRSJ]
• Gustavo Alfonso Garcia Ricardez, Akihiko Yamaguchi, Jun Takamatsu, and Tsukasa Ogasawara:
  Asymmetric Velocity Moderation for human-safe robot control,
  Advanced Robotics, Vol.29, No.17, pp.1111-1125, Sep, 2015. [Taylor & Francis]
• Akihiko Yamaguchi, Christopher G. Atkeson, and Tsukasa Ogasawara:
  Pouring Skills with Planning and Learning Modeled from Human Demonstrations,
  International Journal of Humanoid Robotics, Vol.12, No.3, pp.1550030, July, 2015. [final-draft+col-figs] [World Scientific] [Video]
• Akihiko Yamaguchi, Jun Takamatsu, and Tsukasa Ogasawara:
  DCOB: Action space for reinforcement learning of high DoF robots,
  Autonomous Robots, Vol.34, No.4, pp.327-346, May, 2013. [PDF] [Springer] [Video]
• Akihiko Yamaguchi, Jun Takamatsu, and Tsukasa Ogasawara:
  Constructing Action Space from Basis Functions for Motion Acquisition of Robots by Reinforcement Learning,
  Journal of the Robotics Society of Japan, Vol.29, No.1, pp.55-66, 2011. (in Japanese) [PDF]
• Akihiko Yamaguchi, Norikazu Sugimoto, and Mitsuo Kawato:
  Reinforcement Learning with Reusing Mechanism of Avoidance Actions and its Application to Learning Whole-Body Motions of Multi-Link Robot,
  Journal of the Robotics Society of Japan, Vol.27, No.2, pp.209-220, 2009. (in Japanese) [PDF]

Reviewed international conference paper†

• Akihiko Yamaguchi:
  FingerVision for Tactile Behaviors, Manipulation, and Haptic Feedback Teleoperation,
  in Proceedings of the IEEJ International Workshop on Sensing, Actuation, Motion Control, and Optimization (SAMCON2018), IS2-4, Adachi-ku, Tokyo, Japan, March 6, 2018. [PDF]
• Akihiko Yamaguchi and Christopher G. Atkeson:
  Implementing Tactile Behaviors Using FingerVision,
  in Proceedings of the 17th IEEE-RAS International Conference on Humanoid Robots (Humanoids2017), Birmingham, UK, 2017. [final-draft] [Video]
• Akihiko Yamaguchi and Christopher G. Atkeson:
  Optical Skin For Robots: Tactile Sensing And Whole-Body Vision,
  in the RSS17 Workshop on Tactile Sensing for Manipulation, Robotics: Science and Systems, Massachusetts, USA, 2017. [pdf]
• Akihiko Yamaguchi and Christopher G. Atkeson:
  Stereo Vision of Liquid and Particle Flow for Robot Pouring,
  in Proceedings of the 16th IEEE-RAS International Conference on Humanoid Robots (Humanoids2016), Cancun, Mexico, 2016. [final-draft] [Video]
• Akihiko Yamaguchi and Christopher G. Atkeson:
  Combining Finger Vision and Optical Tactile Sensing: Reducing and Handling Errors While Cutting Vegetables,
  in Proceedings of the 16th IEEE-RAS International Conference on Humanoid Robots (Humanoids2016), Cancun, Mexico, 2016. [final-draft] [Video]
• Akihiko Yamaguchi and Christopher G. Atkeson:
  Differential Dynamic Programming for Graph-Structured Dynamical Systems: Generalization of Pouring Behavior with Different Skills,
  in Proceedings of the 16th IEEE-RAS International Conference on Humanoid Robots (Humanoids2016), Cancun, Mexico, 2016. [final-draft] [Video]
• Akihiko Yamaguchi and Christopher G. Atkeson:
  Model-based Reinforcement Learning with Neural Networks on Hierarchical Dynamic System,
  in the workshop on Deep Reinforcement Learning: Frontiers and Challenges in the 25th International Joint Conference on Artificial Intelligence (IJCAI2016), New York, 2016. [final-draft] [Slides]
• Akihiko Yamaguchi and Christopher G. Atkeson:
  Neural Networks and Differential Dynamic Programming for Reinforcement Learning Problems,
  in Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA2016), pp. 5434-5441, Stockholm, Sweden, May, 2016. [final-draft] [Video]
• Gustavo Alfonso Garcia Ricardez, Akihiko Yamaguchi, Jun Takamatsu, and Tsukasa Ogasawara:
  Human Safety Index based on Impact Severity and Human Behavior Estimation,
  in Proceedings of the 2nd International Conference on Mechatronics and Robotics Engineering (ICMRE 2016), Nice, 2016.
• Akihiko Yamaguchi and Christopher G. Atkeson:
  Differential Dynamic Programming with Temporally Decomposed Dynamics,
  in Proceedings of the 15th IEEE-RAS International Conference on Humanoid Robots (Humanoids2015), pp. 696-703, Seoul, 2015. [final-draft] [Video]
• Akihiko Yamaguchi and Christopher G. Atkeson:
  A Representation for General Pouring Behavior,
  in the workshop on SPAR in the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2015), Hamburg, 2015. [pdf]
• Akihiko Yamaguchi, Masahiro Oshita, Jun Takamatsu, and Tsukasa Ogasawara:
  Experimental Verification of Learning Strategy Fusion for Varying Environments,
  in Proceedings of the 10th ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts (HRI2015), pp. 171-172, Portland, 2015. [final-draft] [ACM]
• Gustavo Alfonso Garcia Ricardez, Akihiko Yamaguchi, Jun Takamatsu, and Tsukasa Ogasawara:
  Human Safety and Efficiency of a Robot Controlled by Asymmetric Velocity Moderation,
  in Proceedings of the 10th ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts (HRI2015), pp. 183-184, Portland, 2015.
• Akihiko Yamaguchi, Christopher G. Atkeson, Scott Niekum, and Tsukasa Ogasawara:
  Learning Pouring Skills from Demonstration and Practice,
  in Proceedings of the 14th IEEE-RAS International Conference on Humanoid Robots (Humanoids2014), pp. 908-915, Madrid, 2014. [final-draft]
• Gustavo Alfonso Garcia Ricardez, Akihiko Yamaguchi, Jun Takamatsu, and Tsukasa Ogasawara:
  Extended Asymmetric Velocity Moderation: a reactive strategy for Human-Safe Robot Control,
  in Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO2013), pp. 450-455, Shenzhen, December, 2013. [IEEE]
• Akihiko Yamaguchi, Jun Takamatsu, and Tsukasa Ogasawara:
  Learning Strategy Fusion for Acquiring Crawling Behavior in Multiple Environments,
  in Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO2013), pp. 605-612, Shenzhen, December, 2013. [final-draft] [IEEE]
• Gustavo Alfonso Garcia Ricardez, Akihiko Yamaguchi, Jun Takamatsu, and Tsukasa Ogasawara:
  Withdrawal Strategy for human Safety based on a Virtual Force Model,
  in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2013), pp. 1119-1124, Tokyo, November 4, 2013. [IEEE]
• Akihiko Yamaguchi, and Tsukasa Ogasawara:
  A Novel Robot-Task-Description for a Variety of Dynamic Behaviors,
  in Proceedings of the 2012 IEEE/SICE International Symposium on System Integration (SII2012), pp.86-92, Fukuoka, Japan, December 16, 2012. [final-draft]
• Akihiko Yamaguchi, Shiori Sato, Kentaro Takemura, Jun Takamatsu, and Tsukasa Ogasawara:
  Style Translation Filter to Change Attribute of Motion,
  in Proceedings of the 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids2012), pp.660-665, Osaka, Japan, December 1, 2012. [final-draft]
• Gustavo Alfonso Garcia Ricardez, Akihiko Yamaguchi, Jun Takamatsu, and Tsukasa Ogasawara:
  Asymmetric Velocity Moderation: a Reactive Strategy for Human Safety,
  in Proceedings of the 10th IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR2012), 56 (S7A-2), Texas, US, November 7, 2012.
• Emarc Magtanong, Akihiko Yamaguchi, Kentaro Takemura, Jun Takamatsu, and Tsukasa Ogasawara:
  Inverse Kinematics Solver for Android Faces with Elastic Skin,
  Latest Advances in Robot Kinematics, pp.181-188, Innsbruck, Austria, June 26, 2012. [final-draft]
• Akihiko Yamaguchi, Jun Takamatsu, and Tsukasa Ogasawara:
  Learning Strategy Fusion to Acquire Dynamic Motion,
  in Proceedings of the 11th IEEE-RAS International Conference on Humanoid Robots (Humanoids2011), pp.247-254, Bled, Slovenia, 2011. [final-draft]
• Akihiko Yamaguchi, and Tsukasa Ogasawara:
  SkyAI: Highly Modularized Reinforcement Learning Library ---Concepts, Requirements, and Implementation---,
  in Proceedings of the 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids2010), pp.118-123, Nashville, TN, US, 2010. [final-draft]
• Akihiko Yamaguchi, Sang-Ho Hyon, and Tsukasa Ogasawara:
  Reinforcement Learning for Balancer Embedded Humanoid Locomotion,
  in Proceedings of the 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids2010), pp.308-313, Nashville, TN, US, 2010. [final-draft]
• Akihiko Yamaguchi, Jun Takamatsu, and Tsukasa Ogasawara:
  Constructing Continuous Action Space from Basis Functions for Fast and Stable Reinforcement Learning,
  in Proceedings of the 18th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN2009), pp.401-407, Toyama, Japan, 2009. [final-draft]
• Akihiko Yamaguchi, Jun Takamatsu, and Tsukasa Ogasawara:
  Constructing Action Set from Basis Functions for Reinforcement Learning of Robot Control,
  in Proceedings of the 2009 IEEE International Conference on Robotics and Automation (ICRA2009), pp.2525-2532, Kobe, Japan, May, 2009. [final-draft]

Other articles†

• Akihiko Yamaguchi:
  On special issue "Robotic Manipulation of Food",
  Journal of the Robotics Society of Japan, Vol.37, No.6, pp.464-465, 2019. (in Japanese) [JRSJ]
• Akihiko Yamaguchi, and Christopher G. Atkeson:
  Robotic Manipulation of Liquids,
  Journal of the Robotics Society of Japan, Vol.37, No.6, pp.477-482, 2019. (in Japanese) [final-draft] [JRSJ]

Invited talk†

• Akihiko Yamaguchi:
  Reinforcement Learning with Skill Library for Complicated Manipulation,
  in the Workshop on Robotic Food Manipulation at the 2019 IEEE-RAS International Conference on Humanoid Robots (Humanoids2019), Oct 15, 2019. [Workshop website]
• Akihiko Yamaguchi:
  FingerVision: Vision-based Tactile Sensor for Proximity and Tactile Perception,
  in the 2nd Workshop on Proximity Perception in Robotics at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2019), Nov 8, 2019. [Workshop website]
• Akihiko Yamaguchi:
  Manipulation Learning Boosted by Visuotactile Sensor FingerVision,
  SICE Hokuriku Chapter Conference, Kanazawa, Ishikawa, August 6, 2018.
• Akihiko Yamaguchi:
  AI-based Robotic Manipulation,
  The 111-th Robotics Seminar of the Robotics Society of Japan on Robotic Manipulation with AI for Industrial Applications, Bunkyo, Tokyo, April 20, 2018.
• Akihiko Yamaguchi:
  FingerVision for Tactile Behaviors, Manipulation, and Haptic Feedback Teleoperation,
  The IEEJ International Workshop on Sensing, Actuation, Motion Control, and Optimization (SAMCON2018), Adachi-ku, Tokyo, Japan, March 6, 2018.
• Akihiko Yamaguchi:
  Robot Learning with Structured Knowledge and Richer Sensing,
  Invited talk in National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, Japan, Aug 24, 2017.
• Akihiko Yamaguchi:
  Robot Learning with Structured Knowledge and Richer Sensing,
  Invited talk in Tohoku University, Sendai, Miyagi, Japan, Aug 24, 2017.
• Akihiko Yamaguchi:
  A Library-based Approach to Intelligent Robot Behaviors,
  Invited talk in Kyushu Institute of Technology, Kyushu, Japan, March 16, 2017.
• Akihiko Yamaguchi:
  Optical Soft Skin For Soft Object Manipulation,
  in the workshop Can We Build Baymax? Part 2: Making Hard Robots Soft Sensors, Skin and Airbags, in the 16th IEEE-RAS International Conference on Humanoid Robots (Humanoids2016), Cancun, Mexico, 2016.
• Akihiko Yamaguchi:
  Unification of Symbolic and Numerical Representations,
  Invited talk in University of Bremen, Bremen, Germany, May 23, 2016.
• Akihiko Yamaguchi:
  Pouring Skills with Planning and Learning Methods,
  Invited talk in Robotics and Mechatronics Center, German Aerospace Center (DLR), Wessling, Germany, October 4, 2015.
• Akihiko Yamaguchi:
  Pouring Skills with Planning and Learning Methods,
  Invited talk in ATR, Japan, November 10, 2015.
• Akihiko Yamaguchi:
  DARPA Robotics Challenge and Artificial Intelligence: Gap between Theory and Practice,
  Invited talk in Nara Institute of Science and Technology, Ikoma, Japan, November 6, 2015. [Slides] (in Japanese)

Unreviewed conference paper†

Selected papers only. Please refer to the publications in Japanese for the full list.

• Fucai Zhu, Akihiko Yamaguchi, Koichi Hashimoto:
  Case study of model-based reinforcement learning with skill library in peeling banana task,
  in Proceedings of the 20th SICE System Integration Division Annual Conference (SI2019), December 14, 2019. [PDF]
• Akihiko Yamaguchi:
  Assistive Utilities with FingerVision for Learning Manipulation of Fragile Objects,
  in Proceedings of the 19th SICE System Integration Division Annual Conference (SI2018), December 15, 2018. [PDF]
• Fucai Zhu, Akihiko Yamaguchi, Koichi Hashimoto:
  Using Human Actors in Robotic Manipulation Study: An Initial Attempt with Peeling Banana Task,
  in Proceedings of the 19th SICE System Integration Division Annual Conference (SI2018), December 15, 2018. [PDF]
• Vorapol Assavasangthong, Akihiko Yamaguchi, and Koichi Hashimoto:
  Nearby Object Detection and Tracking for FingerVision,
  in Proceedings of the JSME Robotics and Mechatronics Conference 2018 (ROBOMECH2018), 2A1-H15, Kitakyushu, Japan, June 5, 2018.
• Akihiko Yamaguchi and Yoshihiro Shibata:
  Mikata Arm + FingerVision --Sensor Rich Robot Platform for Education and Research--,
  in Proceedings of the JSME Robotics and Mechatronics Conference 2018 (ROBOMECH2018), 2A1-H16, Kitakyushu, Japan, June 5, 2018. [PDF]
• Akihiko Yamaguchi and Christopher G. Atkeson:
  Grasp Adaptation Control with Finger Vision: Verification with Deformable and Fragile Objects,
  in Proceedings of the 35th Annual Conference of the Robotics Society of Japan (RSJ2017), 1L3-01, Saitama, September 11-14, 2017. [final-draft] [Video] [Slides]
• Akihiko Yamaguchi, Christopher G. Atkeson, Scott Niekum, Tsukasa Ogasawara:
  Learning Skills from Human Demonstrations: Initial Attempt on a Pouring Task,
  in Proceedings of the 32nd Annual Conference of the Robotics Society of Japan (RSJ2014), 1I3-04, Kyusyu, September 4-6, 2014. [final-draft]
• Gustavo Alfonso Garcia Ricardez, Akihiko Yamaguchi, Masahiro Yoshikawa, Jun Takamatsu, and Tsukasa Ogasawara:
  Improving the Trajectory Scaling Computation of Asymmetric Velocity Moderation,
  in Proceedings of the 32nd Annual Conference of the Robotics Society of Japan (RSJ2014), 2E2-04, Tokyo, September 4-6, 2014.
• Gustavo Alfonso Garcia Ricardez, Phawat Lertariyasakchai, Akihiko Yamaguchi, Jun Takamatsu, and Tsukasa Ogasawara:
  Kinematic Analysis for the Development of Human-Safe Robot Behavior using a Sensor Suit,
  in Proceedings of the 31st Annual Conference of the Robotics Society of Japan (RSJ2013), 1J3-01, Tokyo, September 4-6, 2013.
• Gustavo Alfonso Garcia Ricardez, Akihiko Yamaguchi, Jun Takamatsu, and Tsukasa Ogasawara:
  Efficiency Evaluation of Asymmetric Velocity Moderation based on Task Completion Time,
  in Proceedings of the 2013 JSME Conference on Robotics and Mechatronics (ROBOMEC2013), 2A2-I10, Tsukuba, Japan, May 22-25, 2013.
• Gustavo Alfonso Garcia Ricardez, Akihiko Yamaguchi, Jun Takamatsu, and Tsukasa Ogasawara:
  Velocity Moderation for Providing Human Safety Reactively,
  in proceedings of the 30th Annual Conference of the Robotics Society of Japan (RSJ2012), 4M1-7, Sapporo, September 17-20, 2012.
• Gustavo Alfonso Garcia Ricardez, Satoki Tsuichihara, Atsutoshi Ikeda, Akihiko Yamaguchi, Jun Takamatsu, and Tsukasa Ogasawara:
  RT Component for Analyzing a Motion Script to Implement a Service using the Humanoid Robot HRP-4,
  in Proceedings of the 12th SICE System Integration Division Annual Conference (SI2011), December 23, 2011.
• Emarc Magtanong, Akihiko Yamaguchi, Kentaro Takemura, Jun Takamatsu, and Tsukasa Ogasawara:
  Inverse Kinematics Solver for an Android Face using Neural Network,
  in Proceedings of the 29th Annual Conference of the Robotics Society of Japan (RSJ2011), 1Q3-1, Tokyo, September, 2011. [PDF]
• Akihiko Yamaguchi, Jun Takamatsu, and Tsukasa Ogasawara:
  Utilizing Dynamics and Reward Models in Learning Strategy Fusion,
  in Proceedings of the 2011 JSME Conference on Robotics and Mechatronics (ROBOMEC2011), 1A1-O03, Okayama, Japan, May, 2011. [PDF]
• Akihiko Yamaguchi, Jun Takamatsu, and Tsukasa Ogasawara:
  Fusing Learning Strategies to Learn Various Tasks with Single Configuration,
  IEICE Technical Report, Vol.110, No.461, NC2010-154, pp.159-164, March 2011. [final-draft]
• Akihiko Yamaguchi, and Tsukasa Ogasawara:
  SkyAI: Highly Modularized Reinforcement Learning Library ---Script Interface---,
  in Proceedings of the 28th Annual Conference of the Robotics Society of Japan, RSJ2010AC1I1-4, Nagoya, September, 2010. (in Japanese) [PDF]
• Akihiko Yamaguchi, Jun Takamatsu, and Tsukasa Ogasawara:
  DCOB: Constructing Action Space from Basis Functions for Motion Acquisition of Robots by Reinforcement Learning ---Application to Crawling Task of Actual Small Size Multi-link Robot---,
  in Proceedings of the 2010 JSME Conference on Robotics and Mechatronics (ROBOMEC2010), 2P1-G10, Asahikawa, Japan, June, 2010. (in Japanese) [PDF]
• Akihiko Yamaguchi, Jun Takamatsu, and Tsukasa Ogasawara:
  Composition of Feature Space and State Space Dynamics Models for Model-based Reinforcement Learning,
  IEICE Technical Report, Vol.109, No.1, NC2009-8, pp.7-12, July 2009. [final-draft]

Book†

• Frontier of Sensation-oriented Technology --Creation of Comfort and Amusement--,
  CMC Publishing, 2018. (Section 7 "Introducing Tactile Sensing into Robotic Hands") (in Japanese)
• SINGULARITY,
  Kindai kagaku sya, 2016. (Chap 8 "Robot Learning Changing The Future") (in Japanese)
• OpenCV 2 Programming Book Seisaku-Team:
  OpenCV 2 Programming Book,
  Mynavi, 2011. (Section 4.9) (in Japanese)

Dissertation†

• Akihiko Yamaguchi:
  Highly Modularized Learning System for Behavior Acquisition of Functional Robots,
  Doctoral Dissertation, Nara Institute of Science and Technology, 2011. [PDF]
• Akihiko Yamaguchi:
  Reinforcement Learning with Reusing Mechanism of Avoidance Actions and its Application to Learning Whole-Body Motions of Multi-Link Robot,
  Master's Thesis, Nara Institute of Science and Technology, 2008. (in Japanese) [PDF]
• Akihiko Yamaguchi:
  Event-driven Control and Its Representation ---Towards a Motion Learning of Multi-DOF Robot in Dynamic Environment---,
  Bachelor's Thesis, Kyoto University, 2006. (in Japanese) [PDF] [Video]