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Kazutoshi Tanaka

29 accepted papers

2026

SCU-Hand with Integrated Single-Sheet Valve: A Funnel-Shaped Robotic Hand for Milligram-Scale Powder Handling

Tomoya Takahashi, Yusaku Nakajima, Cristian Camilo Beltran-Hernandez, Yuki Kuroda, Kazutoshi Tanaka, Masashi Hamaya +2
ICRA 2026poster

Laboratory Automation (LA) has the potential to accelerate solid-state materials discovery by enabling continuous robotic operation without human intervention. While robotic systems have been developed for tasks such as powder grinding and X-ray diffraction (XRD) analysis, fully automating powder ha…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

Pose Estimation of a Cable-Driven Serpentine Manipulator Utilizing Intrinsic Dynamics via Physical Reservoir Computing

Kazutoshi Tanaka, Tomoya Takahashi, Masashi Hamaya
IROS 2025

Cable-driven serpentine manipulators hold great potential in unstructured environments, offering obstacle avoidance, multi-directional force application, and a lightweight design. By placing all motors and sensors at the base and employing plastic links, we can further reduce the arm’s weight. To de

Cited by 0Source ↗Scholar ↗
2025

SCU-Hand: Soft Conical Universal Robotic Hand for Scooping Granular Media from Containers of Various Sizes

Tomoya Takahashi, Cristian C. Beltran-Hernandez, Yuki Kuroda, Kazutoshi Tanaka, Masashi Hamaya, Yoshitaka Ushiku
ICRA 2025

Automating small-scale experiments in materials science presents challenges due to the heterogeneous nature of experimental setups. This study introduces the SCU-Hand (Soft Conical Universal Robot Hand), a novel end-effector designed to automate the task of scooping powdered samples from various con

Cited by 4Source ↗Scholar ↗
2025

WAVE: Worm Gear-based Adaptive Variable Elasticity for Decoupling Actuators from External Forces

Moses Gladson Selvamuthu, Tomoya Takahashi, Riichiro Tadakuma, Kazutoshi Tanaka
IROS 2025

Robotic manipulators capable of regulating both compliance and stiffness offer enhanced operational safety and versatility. Here, we introduce Worm Gear-based Adaptive Variable Elasticity (WAVE), a variable stiffness actuator (VSA) that integrates a non-backdrivable worm gear. By decoupling the driv

Cited by 0Source ↗Scholar ↗
2024

Low-Cost Air Hockey Robot Using a Five-Bar Linkage Mechanism Driven by Position-Control Servomotors

Mirai Shinjo, Cristian C. Beltran-Hernandez, Masashi Hamaya, Kazutoshi Tanaka
IROS 2024poster

In human-robot interaction (HRI) research, ball games pose significant challenges that demand robotic solutions that are both cost-effective and user-friendly for non-experts. Air hockey, characterized by safe, non-direct-contact play and a simplified state-action space, emerges as an ideal platform…

Cited by 0Source ↗Scholar ↗
2024

Vision-Language Interpreter for Robot Task Planning

Keisuke Shirai, Cristian C. Beltran-Hernandez, Masashi Hamaya, Atsushi Hashimoto, Shohei Tanaka, Kento Kawaharazuka +3
ICRA 2024poster

Large language models (LLMs) are accelerating the development of language-guided robot planners. Meanwhile, symbolic planners offer the advantage of interpretability. This paper proposes a new task that bridges these two trends, namely, multimodal planning problem specification. The aim is to genera…

Cited by 62Source ↗code ↗Scholar ↗
2024

Visuo-Tactile Zero-Shot Object Recognition with Vision-Language Model

Shiori Ueda, Atsushi Hashimoto, Masashi Hamaya, Kazutoshi Tanaka, Hideo Saito
IROS 2024poster

Tactile perception is vital, especially when distinguishing visually similar objects. We propose an approach to incorporate tactile data into a Vision-Language Model (VLM) for visuo-tactile zero-shot object recognition. Our approach leverages the zero-shot capability of VLMs to infer tactile propert…

Cited by 1Source ↗Scholar ↗
2023

Learning Food Picking without Food: Fracture Anticipation by Breaking Reusable Fragile Objects

Rinto Yagawa, Reina Ishikawa, Masashi Hamaya, Kazutoshi Tanaka, Atsushi Hashimoto, Hideo Saito
ICRA 2023poster

Food picking is trivial for humans but not for robots, as foods are fragile. Presetting foods' physical properties does not help robots much due to the objects' inter- and intra-category diversity. A recent study proved that learning-based fracture anticipation with tactile sensors could overcome th…

Cited by 3Source ↗Scholar ↗
2023

Learning Robotic Assembly by Leveraging Physical Softness and Tactile Sensing

Joaquín Royo-Miquel, Masashi Hamaya, Cristian C. Beltran-Hernandez, Kazutoshi Tanaka
IROS 2023poster

This study aims to achieve autonomous robotic assembly under uncertain conditions arising from imprecise goal positioning and variations in the angle of the grasped part. Soft robots are suitable for such uncertain and contact-rich environments and are capable of insertion tasks with imprecise goal…

Cited by 9Source ↗Scholar ↗
2023

Learning Robotic Powder Weighing from Simulation for Laboratory Automation

Yuki Kadokawa, Masashi Hamaya, Kazutoshi Tanaka
IROS 2023poster

This study focuses on a robotic powder weighing task used in laboratory automation. In this task, a robot weighs a certain amount of powder with a milligram-level target mass using a dispensing spoon. The complex dynamics of the powder, the variations in the materials being weighed, and the need to…

Cited by 7Source ↗Scholar ↗
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 ↗
2023

Twist Snake: Plastic table-top cable-driven robotic arm with all motors located at the base link

Kazutoshi Tanaka, Masashi Hamaya
ICRA 2023poster

Table-top robotic arms for education and research must be low-cost for availability and lightweight and soft for safety. Therefore, as such a robot, this study focuses on designing a plastic table-top cable-driven robotic arm with all motors located at the base link. However, locating all motors at…

Cited by 6Source ↗Scholar ↗
2022

Continuum-Body-Pose Estimation From Partial Sensor Information Using Recurrent Neural Networks

Kazutoshi Tanaka, Yuna Minami, Yuji Tokudome, Katsuma Inoue, Yasuo Kuniyoshi, Kohei Nakajima
RA-L 2022

Soft continuum arms have significant potential for use in various applications due to their extremely high degrees of freedom. For example, these soft arms can be used for grasping and manipulating fragile materials in the deep sea or carrying a human to rescue in unstructured environments. However,

Cited by 19Source ↗Scholar ↗
2022

Quasistatic contact-rich manipulation via linear complementarity quadratic programming

Sotaro Katayarna, Tatsunori Taniai, Kazutoshi Tanaka
IROS 2022poster

Contact-rich manipulation is challenging due to dynamically-changing physical constraints by the contact mode changes undergone during manipulation. This paper proposes a versatile local planning and control framework for contact-rich manipulation that determines the continuous control action under…

Cited by 4Source ↗code ↗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

Immediate Generation of Jump-and-Hit Motions by a Pneumatic Humanoid Robot Using a Lookup Table of Learned Dynamics

Kazutoshi Tanaka, Satoshi Nishikawa, Ryuma Niiyama, Yasuo Kuniyoshi
RA-L 2021

This letter focuses on the jump-and-hit motion of a humanoid robot, wherein a robot instantaneously jumps forward and hits a flying ball in the air, similar to how human players behave in volleyball games. We propose a Immediate Motion generation using a Lookup table of learned dynamics (IMoLo) for

Cited by 14Source ↗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 ↗
2019

High-Speed Humanoid Robot Arm for Badminton Using Pneumatic-Electric Hybrid Actuators

Shotaro Mori, Kazutoshi Tanaka, Satoshi Nishikawa, Ryuma Niiyama, Yasuo Kuniyoshi
RA-L 2019

We describe the development of a robot configured to play badminton, a dynamic sport that requires high accuracy. We used pneumatic-electric hybrid actuators, each combining a pneumatic actuator, with high-speed and lightweight attributes, and an electric motor with good controllability. Our first o

Cited by 40Source ↗Scholar ↗
2018

High-Speed and Lightweight Humanoid Robot Arm for a Skillful Badminton Robot

Shotaro Mori, Kazutoshi Tanaka, Satoshi Nishikawa, Ryuma Niiyama, Yasuo Kuniyoshi
RA-L 2018

Sports, especially badminton, require participants to perform dynamic and skillful motions. Previous robots have had difficulty in performing like a human because of their severe limitations of low operating speed, heavy bodies, and simplistic mechanisms. In this letter, we propose a new robot desig

Cited by 39Source ↗Scholar ↗