Yasunori Toshimitsu

Last Name

Toshimitsu

First Name

Yasunori

ORCID

0000-0003-4896-8742

Organisational unit

09689 - Katzschmann, Robert / Katzschmann, Robert

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Publications 1 - 10 of 11

A Robust Adaptive Approach to Dynamic Control of Soft Continuum Manipulators
Kazemipour, Amirhossein; Fischer, Oliver; Toshimitsu, Yasunori; et al. (2021)
arXiv
Soft robots are made of compliant and deformable materials and can perform tasks challenging for conventional rigid robots. The inherent compliance of soft robots makes them more suitable and adaptable for interactions with humans and the environment. However, this preeminence comes at a cost: their continuum nature makes it challenging to develop robust model-based control strategies. Specifically, an adaptive control approach addressing this challenge has not yet been applied to physical soft robotic arms. This work presents a reformulation of dynamics for a soft continuum manipulator using the Euler-Lagrange method. The proposed model eliminates the simplifying assumption made in previous works and provides a more accurate description of the robot's inertia. Based on our model, we introduce a task-space adaptive control scheme. This controller is robust against model parameter uncertainties and unknown input disturbances. The controller is implemented on a physical soft continuum arm. A series of experiments were carried out to validate the effectiveness of the controller in task-space trajectory tracking under different payloads. The controller outperforms the state-of-the-art method both in terms of accuracy and robustness. Moreover, the proposed model-based control design is flexible and can be generalized to any continuum robotic arm with an arbitrary number of continuum segments.
Dynamic Task Space Control Enables Soft Manipulators to Perform Real-World Tasks
Fischer, Oliver; Toshimitsu, Yasunori; Kazemipour, Amirhossein; et al. (2023)
Advanced Intelligent Systems
Dynamic motions are a key feature of robotic arms, enabling them to perform tasks quickly and efficiently. A majority of real-world soft robots rely on a quasistatic control approach, without taking dynamic characteristics into consideration. As a result, robots with this restriction move slowly and are not able to adequately deal with forces, such as handling unexpected perturbations or manipulating objects. A dynamic approach to control and modeling would allow soft robots to move faster and handle external forces more efficiently. In previous studies, different aspects of modeling, dynamic control, and physical implementation have been examined separately, while their combination has yet to be thoroughly investigated. Herein, the accuracy of the dynamic model of the multisegment continuum robot is improved by adding new elements that include variable stiffness and actuation behavior. Then, this improved model is integrated with state-of-the-art system identification and dynamic task space control and experiments are performed to validate this combination on a real-world manipulator. As a means of encouraging future research on dynamic control for soft robotic manipulators, the source code is made available for modeling, control, and system identification, along with the recipes for fabricating the manipulator.
SoPrA: Fabrication & Dynamical Modeling of a Scalable Soft Continuum Robotic Arm with Integrated Proprioceptive Sensing
Toshimitsu, Yasunori; Wong, Ki Wan; Buchner, Thomas Jakob Konrad; et al. (2021)
arXiv
Due to their inherent compliance, soft robots are more versatile than rigid linked robots when they interact with their environment, such as object manipulation or biomimetic motion, and considered the key element in introducing robots to everyday environments. Although various soft robotic actuators exist, past research has focused primarily on designing and analyzing single components. Limited effort has been made to combine each component to create an overall capable, integrated soft robot. Ideally, the behavior of such a robot can be accurately modeled, and its motion within an environment uses its proprioception, without requiring external sensors. This work presents a design and modeling process for a Soft continuum Proprioceptive Arm (SoPrA) actuated by pneumatics. The integrated design is suitable for an analytical model due to its internal capacitive flex sensor for proprioceptive measurements and its fiber-reinforced fluidic elastomer actuators. The proposed analytical dynamical model accounts for the inertial effects of the actuator's mass and the material properties, and predicts in real-time the soft robot's behavior. Our estimation method integrates the analytical model with proprioceptive sensors to calculate external forces, all without relying on an external motion capture system. SoPrA is validated in a series of experiments demonstrating the model's and sensor's accuracy in estimation. SoPrA will enable soft arm manipulation including force sensing while operating in obstructed environments that disallows exteroceptive measurements.
Adaptive Dynamic Sliding Mode Control of Soft Continuum Manipulators
Kazemipour, Amirhossein; Fischer, Oliver; Toshimitsu, Yasunori; et al. (2022)
2022 International Conference on Robotics and Automation (ICRA)
Soft robots are made of compliant materials and perform tasks that are challenging for rigid robots. However, their continuum nature makes it difficult to develop model-based control strategies. This work presents a robust model-based control scheme for soft continuum robots. Our dynamic model is based on the Euler-Lagrange approach, but it uses a more accurate description of the robot's inertia and does not include oversimplified assumptions. Based on this model, we introduce an adaptive sliding mode control scheme, which is robust against model parameter uncertainties and unknown input disturbances. We perform a series of experiments with a physical soft continuum arm to evaluate the effectiveness of our controller at tracking task-space trajectory under different payloads. The tracking performance of the controller is around 38 % more accurate than that of a state-of-the-art controller, i.e., the inverse dynamics method. Moreover, the proposed model-based control design is flexible and can be generalized to any continuum robotic arm with an arbitrary number of segments. With this control strategy, soft robotic object manipulation can become more accurate while remaining robust to disturbances.
Getting the Ball Rolling: Learning a Dexterous Policy for a Biomimetic Tendon-Driven Hand with Rolling Contact Joints
Toshimitsu, Yasunori; Forrai, Benedek; Cangan, Barnabas Gavin; et al. (2023)
2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids)
Biomimetic, dexterous robotic hands have the potential to replicate much of the tasks that a human can do, and to achieve status as a general manipulation platform. Recent advances in reinforcement learning (RL) frameworks have achieved remarkable performance in quadrupedal locomotion and dexterous manipulation tasks. Combined with GPU-based highly parallelized simulations capable of simulating thousands of robots in parallel, RL-based controllers have become more scalable and approachable. However, in order to bring RL-trained policies to the real world, we require training frameworks that output policies that can work with physical actuators and sensors as well as a hardware platform that can be manufactured with accessible materials yet is robust enough to run interactive policies. This work introduces the biomimetic tendon-driven Faive Hand and its system architecture, which uses tendon-driven rolling contact joints to achieve a 3D printable, robust high-DoF hand design. We model each element of the hand and integrate it into a GPU simulation environment to train a policy with RL, and achieve zero-shot transfer of a dexterous in-hand sphere rotation skill to the physical robot hand.
Leveraging Pretrained Latent Representations for Few-Shot Imitation Learning on an Anthropomorphic Robotic Hand
Liconti, Davide; Toshimitsu, Yasunori; Katzschmann, Robert K. (2024)
2024 IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids)
In the context of imitation learning applied to anthropomorphic robotic hands, the high complexity of the systems makes learning complex manipulation tasks challenging. However, the numerous datasets depicting human hands in various different tasks could provide us with better knowledge regarding human hand motion. We propose a method to leverage multiple large-scale task-agnostic datasets to obtain latent representations that effectively encode motion subtrajectories that we included in a transformer-based behavior cloning method. Our results demonstrate that employing latent representations yields enhanced performance compared to conventional behavior cloning methods, particularly regarding resilience to errors and noise in perception and proprioception. Furthermore, the proposed approach solely relies on human demonstrations, eliminating the need for teleoperation and, therefore, accelerating the data acquisition process. Accurate inverse kinematics for fingertip retargeting ensures precise transfer from human hand data to the robot, facilitating effective learning and deployment of manipulation policies. Finally, the trained policies have been successfully transferred to a real-world 23Dof robotic system.
Adaptive Control of a Soft Continuum Manipulator
Kazemipour, Amirhossein; Fischer, Oliver; Toshimitsu, Yasunori; et al. (2021)
arXiv
Soft robots are made of compliant and deformable materials and can perform tasks challenging for conventional rigid robots. The inherent compliance of soft robots makes them more suitable and adaptable for interactions with humans and the environment. However, this preeminence comes at a cost: their continuum nature makes it challenging to develop robust model-based control strategies. Specifically, an adaptive control approach addressing this challenge has not yet been applied to physical soft robotic arms. This work presents a reformulation of dynamics for a soft continuum manipulator using the Euler-Lagrange method. The proposed model eliminates the simplifying assumption made in previous works and provides a more accurate description of the robot's inertia. Based on our model, we introduce a task-space adaptive control scheme. This controller is robust against model parameter uncertainties and unknown input disturbances. The controller is implemented on a physical soft continuum arm. A series of experiments were carried out to validate the effectiveness of the controller in task-space trajectory tracking under different payloads. The controller outperforms the state-of-the-art method both in terms of accuracy and robustness. Moreover, the proposed model-based control design is flexible and can be generalized to any continuum robotic arm with an arbitrary number of continuum segments.
Dynamic Motion Control of Multi-Segment Soft Robots Using Piecewise Constant Curvature Matched with an Augmented Rigid Body Model
Katzschmann, Robert K.; Della Santina, Cosimo; Toshimitsu, Yasunori; et al. (2019)
2019 2nd IEEE International Conference on Soft Robotics (RoboSoft)
Despite the emergence of many soft-bodied robotic systems, model-based feedback control for soft robots has remained an open challenge. This is largely due to the intrinsic difficulties in designing controllers for systems with infinite dimensions. This work extends our previously proposed formulation for the dynamics of a soft robot from two to three dimensions. The formulation connects the soft robot's dynamic behavior to a rigid-bodied robot with parallel elastic actuation. The matching between the two systems is exact under the hypothesis of Piecewise Constant Curvature. Based on this connection, we introduce a control architecture with the aim of achieving accurate curvature and bending control. This controller accounts for the natural softness of the system moving in three dimensions, and for the dynamic forces acting on the system. The controller is validated in a realistic simulation, together with a kinematic inversion algorithm. The paper also introduces a soft robot capable of three-dimensional motion, that we use to experimentally validate our control strategy. © 2019 IEEE
SoPrA: Fabrication & Dynamical Modeling of a Scalable Soft Continuum Robotic Arm with Integrated Proprioceptive Sensing
Toshimitsu, Yasunori; Wong, Ki Wan; Buchner, Thomas Jakob Konrad; et al. (2021)
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Due to their inherent compliance, soft robots are more versatile than rigid linked robots when they interact with their environment, such as object manipulation or biomimetic motion, and are considered to be the key element in introducing robots to everyday environments. Although various soft robotic actuators exist, past research has focused primarily on designing and analyzing single components. Limited effort has been made to combine each component to create an overall capable, integrated soft robot. Ideally, the behavior of such a robot can be accurately modeled, and its motion within an environment uses its proprioception, without requiring external sensors. This work presents a design and modeling process for a Soft continuum Proprioceptive Arm (SoPrA) actuated by pneumatics. The integrated design is suitable for an analytical model due to its internal capacitive flex sensor for proprioceptive measurements and its fiber-reinforced fluidic elastomer actuators. The proposed analytical dynamical model accounts for the inertial effects of the actuator's mass and the material properties, and predicts in real-time the soft robot's behavior. Our estimation method integrates the analytical model with proprioceptive sensors to calculate external forces, all without relying on an external motion capture system. SoPrA is validated in a series of experiments demonstrating the model's and sensor's accuracy in estimation. SoPrA will enable soft arm manipulation including force sensing while operating in obstructed environments that disallows exteroceptive measurements.
Reliable Aerial Manipulation: Combining Visual Tracking with Range Sensing for Robust Grasping
Blöchlinger, Marc; Toshimitsu, Yasunori; Katzschmann, Robert K. (2025)
2025 IEEE International Conference on Robotics and Automation (ICRA)
Reliable object localization is a critical challenge in drone-based aerial manipulation, particularly when objects are outside the camera's field of view. This paper presents a new approach to enhance drone reliability in aerial grasping tasks by integrating a 1D time-of-flight range sensor with a vision-based localization system. The range sensor, positioned beneath the drone, generates a detailed point cloud of the ground beneath the drone, allowing for precise object localization even when the drone hovers directly above the target. By combining visual tracking with realtime distance measurements, our system achieves a 96 % grasp success rate across 128 trials with diverse objects, representing a significant improvement over previous approaches. This method enables zero-shot grasping without prior knowledge of the objects, increasing versatility and robustness in complex, unstructured environments. The open-source software and hardware design of the platform provide a foundation for further research and development in the field of autonomous aerial manipulation.

Publications 1 - 10 of 11

Yasunori Toshimitsu

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