The Humanoid Lab is a part of the CNRS-AIST JRL, located at AIST, Tsukuba about 5km from the main campus of the University of Tsukuba. It is associated to the university through the Cooperative Graduate School System, which means that graduate students at the university can work at JRL as Trainees and RAs under the supervision of Prof. Kanehiro (Faculty of Cooperative Graduate School, IMIS).

The lab provides a unique opportunity for graduate students to work with Japanese and foreign research scientists on a wide variety of robot platforms and research topics. Our main research subjects include: task and motion planning and control, multimodal interaction with human and surrounding environment through perception, and cognitive robotics.

金広 文男



Most members of our lab are bilingual (some are quadrilingual!), hence, we encourage Japanese-speaking as well as English-speaking students to join our lab.

The lab is always looking for talented and motivated graduate students to join our group. Students must be accepted to the Master's or Doctoral Program in Intelligent and Mechanical Interaction Systems (IMIS), University of Tsukuba through the regular admission procedure (examinations held in summer and winter).

If you're interested, please contact the lab or Prof. Kanehiro directly before you start the application procedure.

(This page is maintained by current students.)

Vision-based Belt Manipulation by Humanoid Robot    
Deformable objects are very common around us in our daily life. Because they have infinitely many degrees of freedom, they present a challenging problem in robotics. Inspired by practical industrial applications, we present our research on using a humanoid robot to take a long, thin and flexible belt out of a bobbin and pick up the bending part of the belt from the ground. By proposing a novel non-prehensile manipulation strategy “scraping” which utilizes the friction between the gripper and the surface of the belt, efficient manipulation can be achieved. In addition, a 3D shape detection algorithm for deformable objects is used during manipulation process. By integrating the novel “scraping” motion and the shape detection algorithm into our multi-objective QP-based controller, we show experimentally humanoid robots can complete this complex task.  

sim2real: Learning Humanoids Locomotion using RL    

Recent advances in deep reinforcement learning (RL) based techniques combined with training in simulation have offered a new approach to developing control policies for legged robots. However, the application of such approaches to real hardware has largely been limited to quadrupedal robots with direct-drive actuators and light-weight bipedal robots with low gear-ratio transmission systems. Application to life-sized humanoid robots has been elusive due to the large sim2real gap arising from their large size, heavier limbs, and a high gear-ratio transmission systems.

In this work, we investigate methods for effectively overcoming the sim2real gap issue for large-humanoid robots for the goal of deploying RL policies trained in simulation to the real hardware.

The link to YouTube video is here.


Enhanced Visual Feedback with Decoupled Viewpoint Control in Immersive Teleoperation using SLAM  
During humanoid robot teleoperation, there is a noticeable delay between the motion of the operator’s and robot’s head. This latency could cause the lag in visual feedback, which decreases the immersion of the system, may cause some dizziness and reduce the efficiency of interaction in teleoperation since operator needs to wait for the real-time visual feedback. To solve this problem, we developed a decoupled viewpoint control solution which allows the operator to obtain the visual feedback changes with low-latency in VR and to increase the reachable visibility range. Besides, we propose a complementary SLAM solution which uses the reconstructed mesh to complement the blank area that is not covered by the real-time robot’s point cloud visual feedback. The operator could sense the robot head’s real-time orientation by observing the pose of the point cloud.  
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Title Authors Conference/Book Year bib pdf
TransFusionOdom: Transformer-based LiDAR-Inertial Fusion Odometry Estimation L. Sun, G. Ding, Y. Qiu, Y. Yoshiyasu, F. Kanehiro IEEE Sensors Journal 2023
Learning Bipedal Walking for Humanoids with Current Feedback R. Singh, Z. Xie, P. Gergondet, F. Kanehiro IEEE Access 2023
Dual-Arm Mobile Manipulation Planning of a Long Deformable Object in Industrial Installation Y. Qin, A. Escande, F. Kanehiro, E. Yoshida IEEE Robotics and Automation Letters 2023
Mc-Mujoco: Simulating Articulated Robots with FSM Controllers in MuJoCo R. Singh, P. Gergondet, F. Kanehiro IEEE/SICE International Symposium on System Integration 2023
CertainOdom: Uncertainty Weighted Multi-task Learning Model for LiDAR Odometry Estimation L. Sun, G. Ding, Y. Yoshiyasu, F. Kanehiro International Conference on Robotics and Biomimetics 2022
Learning Bipedal Walking on Planned Footsteps for Humanoid Robots R. Singh, M. Benallegue, M. Morisawa, R. Cisneros-Limón, F. Kanehiro IEEE-RAS International Conference on Humanoid Robots 2022
Enhanced Visual Feedback with Decoupled Viewpoint Control in Immersive Humanoid Robot Teleoperation using SLAM Y. Chen, L. Sun, M. Benallegue, R. Cisneros-Limón, R. Singh, K. Kaneko, A. Tanguy, G. Caron, K. Suzuki, A. Kheddar, F. Kanehiro IEEE-RAS International Conference on Humanoid Robots 2022
Rapid Pose Label Generation through Sparse Representation of Unknown Objects R. Singh, M. Benallegue, Y. Yoshiyasu, F. Kanehiro IEEE International Conference on Robotics and Automation 2021
Visual SLAM framework based on segmentation with the improvement of loop closure detection in dynamic environments L. Sun, R. Singh, F. Kanehiro Journal of Robotics and Mechatronics 2021
Multi-purpose SLAM framework for Dynamic Environment L. Sun, F. Kanehiro, I. Kumagai, Y. Yoshiyasu IEEE/SICE International Symposium on System Integration 2020
Instance-specific 6-DoF Object Pose Estimation from Minimal Annotations R. Singh, I. Kumagai, A. Gabas, M. Benallegue, Y. Yoshiyasu, F. Kanehiro IEEE/SICE International Symposium on System Integration 2020
APE: A More Practical Approach To 6-Dof Pose Estimation A. Gabas, Y. Yoshiyasu, R. Singh, R. Sagawa, E. Yoshida IEEE International Conference on Image Processing 2020
Name Grade Email
(replace the _*_ with @)
Rohan Pratap Singh Ph.D. 3rd Year rohan-singh_*
Shimpei Masuda Ph.D. 1st Year masuda.shimpei_*
Cheng Hong Ph.D. 1st Year
Yili Qin Graduated (Ph.D.) yili.tan_*
Leyuan Sun Graduated (Ph.D.) son.leyuansun_*
Xinchi Gao Graduated (Masters)
(since 03/2023)