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Takayuki Murooka

5 accepted papers

2022

Grasp Pose Selection Under Region Constraints for Dirty Dish Grasps Based on Inference of Grasp Success Probability through Self-Supervised Learning

Shumpei Wakabayashi, Shingo Kitagawa, Kento Kawaharazuka, Takayuki Murooka, Kei Okada, Masayuki Inaba
ICRA 2022poster

In the literature on object grasping, the robot often determines the grasp point and posture from visual information. They predict the grasping point uniquely from the object's shape characteristics. However, as a practical matter, there are cases where there are constraints on grasp point due to th…

Cited by 2Source ↗Scholar ↗
2022

Sample-Efficient Learning of Deformable Linear Object Manipulation in the Real World Through Self-Supervision

Robert Lee, Masashi Hamaya, Takayuki Murooka, Yoshihisa Ijiri, Peter Corke
RA-L 2022

Deformable object manipulation has potential for a wide range of real-world applications, but is still largely unsolved due to the complex dynamics and difficulty of state estimation. Learning-based approaches have recently accelerated progress, but generally depend heavily on large simulated datase

Cited by 21Source ↗Scholar ↗
2021

An analytical diabolo model for robotic learning and control

Felix von Drigalski, Devwrat Joshi, Takayuki Murooka, Kazutoshi Tanaka, Masashi Hamaya, Yoshihisa Ijiri
ICRA 2021poster

In this paper, we present a diabolo model that can be used for training agents in simulation to play diabolo, as well as running it on a real dual robot arm system. We first derive an analytical model of the diabolo-string system and compare its accuracy using data recorded via motion capture, which…

Cited by 9Source ↗Scholar ↗
2020

Diabolo Orientation Stabilization by Learning Predictive Model for Unstable Unknown-Dynamics Juggling Manipulation

Takayuki Murooka, Kei Okada, Masayuki Inaba
IROS 2020poster

Juggling manipulation is one of difficult manipulation to acquire since some of such manipulation is unstable and also its physical model is unknown due to the complex non-prehensile manipulation. To acquire these unstable unknown-dynamics juggling manipulation, we propose a method for designing the…

Cited by 6Source ↗Scholar ↗
2020

EXI-Net: EXplicitly/Implicitly Conditioned Network for Multiple Environment Sim-to-Real Transfer

Takayuki Murooka, Masashi Hamaya, Felix von Drigalski, Kazutoshi Tanaka, Yoshihisa Ijiri
CoRL 2020

Sim-to-real transfer is attractive for robot learning, as it avoids the high cost of collecting data with real robots, but transferring agents from simulation to the real world is challenging. Previous studies have presented promising methods to solve this problem, but they may fail when a wider ran

Cited by 0Source ↗PDF ↗Scholar ↗