Author name cluster

Kazutoshi Tanaka

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

21 papers

1 author row

IROS Conference 2025 Conference Paper

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

Kazutoshi Tanaka
Tomoya Takahashi
Masashi Hamaya

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 demonstrate this concept, we developed a 9-degree-of-freedom cable-driven serpentine manipulator with an arm length of 545 mm and a total mass of only 308 g. However, this design introduces flexibility-induced variations, such as cable slack, elongation, and link deformation. These variations result in discrepancies between analytical predictions and actual link positions, making pose estimation more challenging. To address this challenge, we propose a physical reservoir computing based pose estimation method that exploits the manipulator’s intrinsic nonlinear dynamics as a high-dimensional reservoir. Experimental results show a mean pose error of 4. 3 mm using our method, compared to 4. 4 mm with a baseline long short-term memory network and 39. 5 mm with an analytical approach. This work provides a new direction for control and perception strategies in lightweight cable-driven serpentine manipulators leveraging their intrinsic dynamics.

ICRA Conference 2025 Conference Paper

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

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 container sizes using a robotic arm. The SCU-Hand employs a flexible, conical structure that adapts to different container geometries through deformation, maintaining consistent contact without complex force sensing or machine learning-based control methods. Its reconfigurable mechanism allows for size adjustment, enabling efficient scooping from diverse container types. By combining soft robotics principles with a sheet-morphing design, our end-effector achieves high flexibility while retaining the necessary stiffness for effective powder manipulation. We detail the design principles, fabrication process, and experimental validation of the SCU-Hand. Experimental validation showed that the scooping capacity is about 20% higher than that of a commercial tool, with a scooping performance of more than 95% for containers of sizes between 67 mm to 110 mm. This research contributes to laboratory automation by offering a cost-effective, easily implementable solution for automating tasks such as materials synthesis and characterization processes.

IROS Conference 2025 Conference Paper

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

Moses Gladson Selvamuthu
Tomoya Takahashi
Riichiro Tadakuma
Kazutoshi Tanaka

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 driving motor from external forces using this gear, WAVE enables precise force transmission to the joint, while absorbing positional discrepancies through compliance. WAVE is protected from excessive loads by converting impact forces into elastic energy stored in a spring. In addition, the actuator achieves continuous joint stiffness modulation by changing the spring’s precompression length. We demonstrate these capabilities, experimentally validate the proposed stiffness model, show that motor loads approach zero at rest–even under external loading–and present applications using a manipulator with WAVE. This outcome showcases the successful decoupling of external forces. The protective attributes of this actuator allow for extended operation in contact-intensive tasks, and for robust robotic applications in challenging environments.

IROS Conference 2024 Conference Paper

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

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 for such research. Despite the availability of various air hockey robots, their high cost and complexity have limited widespread use among researchers requiring robotics expertise. Addressing this gap, we introduce a low-cost, accessible air hockey robot designed to facilitate HRI studies. Featuring a lightweight five-bar linkage mechanism powered by low-cost servomotors for position control, this robot combines efficiency with ease of use. The complete robot’s cost is estimated at $346. 8, with the arm weighing a mere 19 grams. The robot precisely returns the puck by intermittently adjusting its target joint positions, achieving a play with an average return error of 42. 6 mm. These characteristics affirm the robot’s potential as a valuable tool for advancing HRI research.

ICRA Conference 2024 Conference Paper

Vision-Language Interpreter for Robot Task Planning

Keisuke Shirai
Cristian C. Beltran-Hernandez
Masashi Hamaya
Atsushi Hashimoto 0001
Shohei Tanaka
Kento Kawaharazuka
Kazutoshi Tanaka
Yoshitaka Ushiku

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 generate a problem description (PD), a machine-readable file used by the planners to find a plan. By generating PDs from language instruction and scene observation, we can drive symbolic planners in a language-guided framework. We propose a Vision-Language Interpreter (ViLaIn), a new framework that generates PDs using state-of-the-art LLM and vision-language models. ViLaIn can refine generated PDs via error message feedback from the symbolic planner. Our aim is to answer the question: How accurately can ViLaIn and the symbolic planner generate valid robot plans? To evaluate ViLaIn, we introduce a novel dataset called the problem description generation (ProDG) dataset. The framework is evaluated with four new evaluation metrics. Experimental results show that ViLaIn can generate syntactically correct problems with more than 99% accuracy and valid plans with more than 58% accuracy. Our code and dataset are available at https://github.com/omron-sinicx/ViLaIn.

IROS Conference 2024 Conference Paper

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

Shiori Ueda
Atsushi Hashimoto 0001
Masashi Hamaya
Kazutoshi Tanaka
Hideo Saito 0001

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 properties from the names of tactilely similar objects. The proposed method translates tactile data into a textual description solely by annotating object names for each tactile sequence during training, making it adaptable to various contexts with low training costs. The proposed method was evaluated on the FoodReplica and Cube datasets, demonstrating its effectiveness in recognizing objects that are difficult to distinguish by vision alone.

ICRA Conference 2023 Conference Paper

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

Rinto Yagawa
Reina Ishikawa
Masashi Hamaya
Kazutoshi Tanaka
Atsushi Hashimoto 0001
Hideo Saito 0001

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 this problem; however, the method trains the model for each food to deal with intra-category differences, and tuning robots for each food leads to an undesirable amount of food consumption. This study proposes a novel framework for learning food-picking tasks without consuming foods. The key idea is to leverage the object-breaking experiences of several reusable fragile objects instead of consuming real foods while making the picking ability object-invariant with domain generalization (DG). In real-robot experiments, we trained a model with reusable objects (toy blocks, ping-pong balls, and jellies), selected based on the three common fracture types (crack, rupture, and crush). We then tested the model with four real food objects (tofu, bananas, potato chips, and tomatoes). The results showed that the proposed combination of reusable objects' breaking experiences and DG is effective for the food-picking task.

IROS Conference 2023 Conference Paper

Learning Robotic Assembly by Leveraging Physical Softness and Tactile Sensing

Joaquín Royo-Miquel
Masashi Hamaya
Cristian C. Beltran-Hernandez
Kazutoshi Tanaka

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 positions. However, we may also struggle to handle further uncertainty, such as variations in grasping pose. To address the challenge posed by multiple sources of uncertainty, we equipped the soft robot with a tactile sensor. Our key insight is that tactile signal patterns are closely linked to the subtask transitions in an assembly process, specifically from the search to insertion subtasks. We hypothesize soft robots could complete the task by exploring the transition via tactile signals, even in scenarios with imprecise goal positions and grasp misalignment. To this end, we develop an anomaly detection model using a Variational Autoencoder to identify the timing of these transitions. We then employ learning and heuristic-based controllers to navigate the peg tip to the hole and perform the insertion. Our method was validated through real-robot experiments using a soft wrist and a vision-based tactile sensor. The results demonstrate that our method achieves a 100% success rate in scenarios with less uncertain goal pose ( $\sigma=2\text{mm}$ ) and grasp misalignment (up to 5°) and a 70% success rate in scenarios with uncertain goal pose ( $\sigma=10\text{mm}$ ) and grasp misalignment (up to 20°). Moreover, our anomaly detection model can generalize to different peg diameters without additional training.

IROS Conference 2023 Conference Paper

Learning Robotic Powder Weighing from Simulation for Laboratory Automation

Yuki Kadokawa
Masashi Hamaya
Kazutoshi Tanaka

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 balance conservative and aggressive actions are significant challenges in the robotics field. Therefore, learning approaches are critical for this task. However, many learning interactions in real-world environments require substantial efforts to clean the spread powder. To overcome this issue, this study employs a sim-to-real transfer learning approach using a domain randomization (DR) technique. This enables the robot to weigh various powders with a small target mass and alleviates the burden of collecting data in a real-world environment. Herein, we formulated weighing manipulation as a reinforcement learning problem. Besides, we developed a powder weighing simulator and carefully selected the dynamics parameters used for DR to adapt to unseen environments. A recurrent neural network-based policy was adopted considering the balance of conservative and aggressive actions. The sim-to-real zero-shot transfer experiments demonstrated that the robot completed the weighing tasks with an average weighing error of 0. 1 - 0. 2 mg for different powder materials and target masses (5 - 15 mg). Overall, this approach shows promising results and can be useful for automating laboratory tasks that involve weighing powders.

IROS Conference 2023 Conference Paper

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
Yoshitaka Ushiku
Kanta Ono

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 small size. Robotic powder grinding would solve this problem, but it is a challenging task for robots, as it requires observing the powder state and generating appropriate motions. Our previous study proposed a robotic powder grinding system using visual feedback. Although visual feedback is helpful for observing the powder distribution, the particle size during the grinding process remains invisible, leading to suboptimal robot actions. In some cases, the robot chose to gather the powder even though continuing to grind instead would have produced finer powder. In this paper, we present a multi-modal robotic grinding system that utilizes both audio and visual feedback. It makes use of the grinding sound which carries information about the grinding progress, as the particle size strongly affects the audio intensity. The audio feedback enables the robot to grind until the powder is sufficiently fine. In our experiments, the robot ground 80. 5% of the powder to a particle size smaller than $250\ \mu\mathrm{m}$ with audio and visual feedback and 68% without audio feedback, indicating that multi-modal feedback is an effective tool to produce finer powder. We conclude that the addition of audio feedback provides crucial information to the robot, allowing it to better understand the progress of the grinding process and make more optimal decisions. This robot system can be used to prepare samples in material science experiments and analyze the grinding process.

ICRA Conference 2023 Conference Paper

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

Kazutoshi Tanaka
Masashi Hamaya

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 the base link results in a significant distance between a driving motor and driven joint, increases the number of parts for the force transmission, and increases the risk of a cable loosening and coming off of a pulley. To overcome these issues, this study proposed a novel cable-driven robotic arm named Twist Snake. We designed a joint composition of Twist Snake to minimize the number of parts for the force transmission. In addition, it has a compact cable-pretension/termination-mechanism and covering parts to prevent the cable from loosening and coming off of the pulley. The arm comprised 475 mm long moving links with an 802 g. The feasibility of the arm was experimentally demonstrated by contact rich tasks, the insertion of a toy peg into a hole and swiping a whiteboard with a cleaner. The optimization of the proposed design and the development of a learning method for the arm that leverages contact will be investigated in future work.

IROS Conference 2022 Conference Paper

Quasistatic contact-rich manipulation via linear complementarity quadratic programming

Sotaro Katayama
Tatsunori Taniai
Kazutoshi Tanaka

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 variable contact modes online. We model the physical characteristics of contact-rich manipulation by quasistatic dynamics and complementarity constraints. We then propose a linear complementarity quadratic program (LCQP) to efficiently determine the control action that implicitly includes the decisions on the contact modes under these constraints. In the LCQP, we relax the complementarity constraints to alleviate ill-conditioned problems that are typically caused by measure noises or model miss-matches. We conduct dynamical simulations on a 3D physical simulator and demonstrate that the proposed method can achieve various contact-rich manipulation tasks by determining the control action including the contact modes in real-time.

IROS Conference 2022 Conference Paper

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
Yoshitaka Ushiku
Kanta Ono

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 material science applications that observe the powder's state to improve the grinding outcome. We developed a soft jig consisting of off-the-shelf gel materials and 3D-printed parts, which can be used with any robot arm to perform powder grinding. The jig's physical softness allows for safe grinding without force sensing. In addition, we developed a visual feedback system that observes the powder distribution and decides where to grind and when to gather. The results showed that our system could grind 79 percent of the powder to a particle size smaller than 200 μm by using the soft jig and visual feedback. This ratio was 57% when using only the soft jig without feedback. Our system can be used immediately in laboratories to alleviate the workload of researchers.

ICRA Conference 2021 Conference Paper

An analytical diabolo model for robotic learning and control

Felix von Drigalski
Devwrat Joshi
Takayuki Murooka
Kazutoshi Tanaka
Masashi Hamaya
Yoshihisa Ijiri

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 we release as a public dataset of skilled play with diabolos of different dynamics. We show that our model outperforms a deep-learning-based predictor, both in terms of precision and physically consistent behavior. Next, we describe a method based on optimal control to generate robot trajectories that produce the desired diabolo trajectory, as well as a system to transform higher-level actions into robot motions. Finally, we test our method on a real robot system playing the diabolo, and throw it to and catch it from a human player.

IROS Conference 2021 Conference Paper

Learning Robotic Contact Juggling

Kazutoshi Tanaka
Masashi Hamaya
Devwrat Joshi
Felix von Drigalski
Ryo Yonetani
Takamitsu Matsubara
Yoshihisa Ijiri

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 from trial and error via model-based reinforcement learning (MBRL). The key insight is that complex robot-ball interactions of the contact juggling actually consist of a small set of simple dynamics that each corresponds to a distinct interaction "primitive" such as touching and releasing the ball. Accordingly, we develop a tailored MBRL method that incrementally fits a set of simple dynamics models to the movements of a robot and a ball while also learning a switching model that can select a proper dynamics model depending on the current state and action. The learned model can then be used in an MBRL framework to seek optimal juggling control. We demonstrated the effectiveness of our approach on a simulator of contact juggling performed by a robotic arm.

ICRA Conference 2021 Conference Paper

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

Felix von Drigalski
Kennosuke Hayashi
Yifei Huang 0002
Ryo Yonetani
Masashi Hamaya
Kazutoshi Tanaka
Yoshihisa Ijiri

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 visual recognition can provide efficient pose estimates, it tends to suffer from low precision due to noise, occlusions and calibration errors. On the other hand, tactile fingertip sensors can provide precise complementary information, but their low durability significantly limits their use in real-world applications. To get the best of both worlds, we propose an efficient method for in-hand pose estimation using off-the-shelf cameras and robot wrist force sensors, which requires no precise camera calibration. The key idea is to utilize visual and contact information adaptively to maximally reduce the uncertainty about the in-hand object pose in a Bayesian state estimation framework. As most of the uncertainty can be resolved from visual observations, our approach reduces the number of physical environment interactions while keeping a high pose estimation accuracy. Our experimental evaluation demonstrates that our approach can estimate object poses with sub-mm precision with an off-the-shelf camera and force-torque sensor.

ICRA Conference 2021 Conference Paper

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

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-free fashion, which still lacks sample efficiency on a practical level. In this work, we develop a novel transfer RL method named TRANSfer learning by Aggregating dynamics Models (TRANS-AM). TRANS-AM is based on model-based RL (MBRL) for its high-level sample efficiency, and only requires dynamics models to be collected from source environments. Specifically, it learns to aggregate source dynamics models adaptively in an MBRL loop to better fit the state-transition dynamics of target environments and execute optimal actions there. As a case study to show the effectiveness of this proposed approach, we address a challenging contact-rich peg-in-hole task with variable hole orientations using a soft robot. Our evaluations with both simulation and real-robot experiments demonstrate that TRANS-AM enables the soft robot to accomplish target tasks with fewer episodes compared when learning the tasks from scratch.

IROS Conference 2020 Conference Paper

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
Yoshihisa Ijiri

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-hole task. However, as many robot tasks require the precision of rigid robots, and their performance would degrade when simply adding compliance, it has been difficult to take advantage of physical softness in real applications. A wrist that could switch between soft and rigid modes could solve this problem, but actuators with sufficient strength for this state transition would increase the size and weight of the module and decrease the payload of the robot. To solve this problem, we propose a novel design of a soft module consisting of a cable-driven mechanism, which allows the robot end effector to change between soft and rigid mode while being very compact and light. The module effectively combines the advantages of soft and rigid robots, and can be retrofitted to existing robots and grippers while preserving the characteristics of the robotic system. We evaluate the effectiveness of our proposed design through experiments modeling assembly tasks, and investigate design parameters quantitatively.

ICRA Conference 2020 Conference Paper

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
Yoshihisa Ijiri

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 are prohibitive when the target parts change often, so a flexible method to localize parts with high accuracy after grasping is desired. To solve this problem, we propose a method that can estimate the position of an object in the robot's hand to sub-millimeter precision, and can improve its estimate incrementally, using only minimal calibration and a force sensor. Our method is applicable to any robotic gripper and any rigid object that the gripper can hold, and requires only a force sensor. We demonstrate that the method can determine the position of an object to a precision of under 1 mm without using any part-specific jigs or equipment.

ICRA Conference 2020 Conference Paper

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
Yoshihisa Ijiri

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 maintenance. Physical softness allows the robot to interact with the environment easily. We expect soft robots to perform assembly tasks without the need for high frequency force/torque controllers and sensors. However, specific data-driven approaches are needed to deal with complex models involving nonlinearity and hysteresis. If we were to apply these approaches directly, we would be required to collect very large amounts of training data. To solve this problem, we argue that by leveraging softness and environmental constraints, a robot can complete tasks in lower dimensional state and action spaces, which could greatly facilitate the exploration of appropriate assembly skills. Then, we apply a highly efficient model-based reinforcement learning method to lower dimensional systems. To verify our method, we perform a simulation for peg-in-hole tasks. The results show that our method learns the appropriate skills faster than an approach that does not consider lower dimensional systems. Moreover, we demonstrate that our method works on a real robot equipped with a compliant module on the wrist.

IROS Conference 2020 Conference Paper

Learning Soft Robotic Assembly Strategies from Successful and Failed Demonstrations

Masashi Hamaya
Felix von Drigalski
Takamitsu Matsubara
Kazutoshi Tanaka
Robert Lee
Chisato Nakashima
Yoshiya Shibata
Yoshihisa Ijiri

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 human demonstrations. Our key insight is that the failed demonstrations can be used as constraints to avoid failed behaviors. To this end, we developed a teaching device with which humans can intuitively provide various demonstrations. Moreover, we leverage Physically-Consistent Gaussian Mixture Models to clearly assign Gaussian components to the successful and failed trials. We then create the reference trajectories via Gaussian Mixture Regressions, which fit the successful demonstrations while considering the failed ones. Finally, we apply a sample- efficient deep model-based reinforcement learning method to obtain robust strategies with a few interactions. To validate our method, we developed a real-robot experimental system composed of a rigid collaborative robot arm with a compliant wrist and the teaching device. Our results demonstrated that our method learned the assembly strategies with a higher success rate than when using only successful demonstrations.