Robot Learning
Research Staff
-
Professor
Takamitsu MATSUBARA -
Affiliate Professor
Kenji SUGIMOTO -
Assistant Professor
Yoshihisa TSURUMINE -
Assistant Professor
Hikaru SASAKI -
Assistant Professor
Kenta HANADA -
Assistant Professor
Takuya KIYOKAWA -
Affiliate Assistant Professor
Taisuke KOBAYSAHI
Research Areas

Fig.1 Deep reinforcement learning for cloth manipulation

Fig.2 Object search with Gaussian processes

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.