AIConfPaper
← Search

Felix Von Drigalski

14 accepted papers

2023

Robotic Powder Grinding with Audio-Visual Feedback for Laboratory Automation in Materials Science

Yusaku Nakajima, Masashi Hamaya, Kazutoshi Tanaka, Takafumi Hawai, Felix von Drigalski, Yasuo Takeichi +2
IROS 2023poster

This study focuses on the powder grinding process, which is a necessary step for material synthesis in materials science experiments. In material science, powder grinding is a time-consuming process that is typically executed by hand, as commercial grinding machines are unsuitable for samples of sma…

Cited by 1Source ↗Scholar ↗
2022

Robotic Powder Grinding with a Soft Jig for Laboratory Automation in Material Science

Yusaku Nakajima, Masashi Hamaya, Yuta Suzuki, Takafumi Hawai, Felix von Drigalski, Kazutoshi Tanaka +2
IROS 2022poster

Grinding materials into a fine powder is a time-consuming task in material science that is generally performed by hand, as current automated grinding machines might not be suitable for preparing small-sized samples. This study presents a robotic powder grinding system for laboratory automation in ma…

Cited by 11Source ↗Scholar ↗
2022

Uncertainty-Aware Manipulation Planning Using Gravity and Environment Geometry

Felix von Drigalski, Kazumi Kasaura, Cristian C. Beltran-Hernandez, Masashi Hamaya, Kazutoshi Tanaka, Takamitsu Matsubara
RA-L 2022

Factory automation robot systems often depend on specially-made jigs that precisely position each part, which increases the system's cost and limits flexibility. We propose a method to determine the 3D pose of an object with high precision and confidence, using only parallel robotic grippers and no

Cited by 9Source ↗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 ↗
2021

Learning Robotic Contact Juggling

Kazutoshi Tanaka, Masashi Hamaya, Devwrat Joshi, Felix von Drigalski, Ryo Yonetani, Takamitsu Matsubara +1
IROS 2021poster

Robotic contact juggling is a challenging task in which robots must control the movement of a ball rapidly and indirectly without holding it while keeping the ball in and sometimes out of contact with the robot’s body. In this work, we address the problem of learning such robotic contact juggling fr…

Cited by 4Source ↗Scholar ↗
2021

Precise Multi-Modal In-Hand Pose Estimation using Low-Precision Sensors for Robotic Assembly

Felix von Drigalski, Kennosuke Hayashi, Yifei Huang, Ryo Yonetani, Masashi Hamaya, Kazutoshi Tanaka +1
ICRA 2021poster

In industrial assembly tasks, the in-hand pose of grasped objects needs to be known with high precision for subsequent manipulation tasks such as insertion. This problem (in-hand-pose estimation) has traditionally been addressed using visual recognition or tactile sensing. On the one hand, while vis…

Cited by 36Source ↗Scholar ↗
2021

Robotic Learning From Advisory and Adversarial Interactions Using a Soft Wrist

Masashi Hamaya, Kazutoshi Tanaka, Yoshiya Shibata, Felix von Drigalski, Chisato Nakashima, Yoshihisa Ijiri
RA-L 2021

In this letter, we developed a novel learning framework from physical human-robot interactions. Owing to human domain knowledge, such interactions can be useful for facilitation of learning. However, applying numerous interactions for training data might place a burden on human users, particularly i

Cited by 6Source ↗Scholar ↗
2021

TRANS-AM: Transfer Learning by Aggregating Dynamics Models for Soft Robotic Assembly

Kazutoshi Tanaka, Ryo Yonetani, Masashi Hamaya, Robert Lee, Felix von Drigalski, Yoshihisa Ijiri
ICRA 2021poster

Practical industrial assembly scenarios often require robotic agents to adapt their skills to unseen tasks quickly. While transfer reinforcement learning (RL) could enable such quick adaptation, much prior work has to collect many samples from source environments to learn target tasks in a model-fre…

Cited by 18Source ↗Scholar ↗
2020

A Compact, Cable-driven, Activatable Soft Wrist with Six Degrees of Freedom for Assembly Tasks

Felix von Drigalski, Kazutoshi Tanaka, Masashi Hamaya, Robert Lee, Chisato Nakashima, Yoshiya Shibata +1
IROS 2020poster

Physical softness has been proposed to absorb impacts when establishing contact with a robot or its workpiece, to relax control requirements and improve performance in assembly and insertion tasks. Previous work has focused on special end effector solutions for isolated tasks, such as the peg-in-hol…

Cited by 35Source ↗Scholar ↗
2020

Contact-based in-hand pose estimation using Bayesian state estimation and particle filtering

Felix von Drigalski, Shohei Taniguchi, Robert Lee, Takamitsu Matsubara, Masashi Hamaya, Kazutoshi Tanaka +1
ICRA 2020poster

In industrial assembly tasks, the position of an object grasped by the robot has to be known with high precision in order to insert or place it. In real applications, this problem is commonly solved by jigs that are specially produced for each part. However, they significantly limit flexibility and…

Cited by 30Source ↗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 ↗
2020

Learning Robotic Assembly Tasks with Lower Dimensional Systems by Leveraging Physical Softness and Environmental Constraints

Masashi Hamaya, Robert Lee, Kazutoshi Tanaka, Felix von Drigalski, Chisato Nakashima, Yoshiya Shibata +1
ICRA 2020poster

In this study, we present a novel control framework for assembly tasks with a soft robot. Typically, existing hard robots require high frequency controllers and precise force/torque sensors for assembly tasks. The resulting robot system is complex, entailing large amounts of engineering and maintena…

Cited by 34Source ↗Scholar ↗
2020

Learning Soft Robotic Assembly Strategies from Successful and Failed Demonstrations

Masashi Hamaya, Felix von Drigalski, Takamitsu Matsubara, Kazutoshi Tanaka, Robert Lee, Chisato Nakashima +2
IROS 2020poster

Physically soft robots are promising for robotic assembly tasks as they allow stable contacts with the environment. In this study, we propose a novel learning system for soft robotic assembly strategies. We formulate this problem as a reinforcement learning task and design the reward function from h…

Cited by 24Source ↗Scholar ↗
2018

A Universal Gripper Using Optical Sensing to Acquire Tactile Information and Membrane Deformation

Tatsuya Sakuma, Felix Von Drigalski, Ming Ding, Jun Takamatsu, Tsukasa Ogasawara
IROS 2018poster

The universal gripper has attracted attention due to its simple structure and advanced grasping ability for irregularly shaped objects. In this research, we propose a novel design for a granular-jamming-based gripper which uses a transparent filling and a semi-transparent membrane to allow optical s…

Cited by 39Source ↗Scholar ↗