Robot Learning

Research Staff

  • Professor Takamitsu MATSUBARA

    Professor
    Takamitsu MATSUBARA

  • Affiliate Professor Kenji SUGIMOTO

    Affiliate Professor
    Kenji SUGIMOTO

  • Assistant Professor Yoshihisa TSURUMINE

    Assistant Professor
    Yoshihisa TSURUMINE

  • Assistant Professor Hikaru SASAKI

    Assistant Professor
    Hikaru SASAKI

  • Assistant Professor Kenta HANADA

    Assistant Professor
    Kenta HANADA

  • Assistant Professor Takuya KIYOKAWA

    Assistant Professor
    Takuya KIYOKAWA

  • Affiliate Assistant Professor Taisuke KOBAYSAHI

    Affiliate Assistant Professor
    Taisuke KOBAYSAHI

Research Areas

Fig.1 Deep reinforcement learning for cloth manipulation

Fig.1 Deep reinforcement learning for cloth manipulation

Fig.2 Object search with Gaussian processes

Fig.2 Object search with Gaussian processes

Fig.3 Object shape estimation from tactile sensing

Fig.3 Object shape estimation from tactile sensing

Machine learning algorithms for real world robots

  • (Deep) reinforcement learning
  • (Deep) imitation learning
  • Deep learning for dynamical systems
  • Active perception
  • Human-in-the-loop optimization

Real world applications

  • Smart manufacturing
  • Human-assistive technology (exoskeleton robots, EMG interfaces etc.)
  • Chemical plant modeling and control
  • Vehicle autopiloting

Research equipment

  • Nextage robot (Kawada)
  • Baxter robot (Rethink)
  • UR5 and UR3 (Universal robots)
  • OP3 humanoid robot (Robotis)
  • Various sensors (motion capture systems, EMG sensors, etc.)

Collaborators

University of Technology Sydney (Australia), Radboud Univ. (The Netherland), Karlsruhe Institute of Technology (Germany), Edinburgh Univ. (UK), LAAS-CNRS (France), ATR, AIST, Shinshu Univ., Ritsumeikan Univ. Kansai Univ. (Japan), etc.

Research Statement

Robot learning (machine learning for robots) is an interdisciplinary field of various fields such as machine learning, artificial intelligence, robot engineering, control engineering, signal processing, optimization, and mechatronics. You may be able to find your approach by utilizing your field of expertise, skills, and experience (robot contests, programming contests, work, etc.). Please challenge yourself within robot learning research.