arXiv subjects

Sho Sakaino

Publications and source records attributed to Sho Sakaino.

At least 19 recordsLinked to original sources

arXiv2026

Reproducible Dynamic Parameter Identification for a Low-Cost Robot Arm: A Positive-Definiteness Audit for Model Acceptance

Dynamic parameter identification of low-cost robot arms is challenging because limited sensing and drivetrain nonidealities can yield models that predict measured torques well but are physically unsuitable for model-based control. This paper presents a reproducible dynamic parameter identification pipeline for CRANE-X7, a low-cost seven-degree-of-freedom arm driven by modular smart actuators. A 39-parameter OpenSYMORO base-parameter model is identified without CAD inertial data using fully specified single-joint and adjacent-pair excitation, ordinary least squares, a conditional semidefinite-programming projection, and closed-loop input error refinement. Model acceptance is decided by a separate positive-definiteness audit of the identified inertia matrix over 221,875 sampled configurations. Experiments cover 40 identification trajectories at four sampling intervals and three held-out validation trajectories. The reduced model improves held-out prediction over the full 65-parameter model on all seven identification trajectories used for the model comparison. Fixed-configuration analyses show the distinct role of the feasibility audit: models with similar torque predictions can produce unstable acceleration-resolved dynamics and reverse the direction of the inertia inversion. For the selected identification trajectory, two executions separated by 26 days yield a 1.25% relative spread in held-out root-mean-square error. The accepted model passes audits with five random seeds, and sensitivity analyses quantify the effects of the sampling interval and of torque-constant uncertainty. The complete trajectory specification and numerical record support independent implementation and comparison. These results establish predictive performance, inertia-matrix feasibility, and repeatability as complementary criteria for evaluatingdynamic models of low-cost arms.

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arXiv2026

VFILC: Accurate Frequency Extrapolations in Imitation Learning via Sampling Frequency ILC

Conventional neural network (NN)-based imitation learning methods for variable-speed motion either restricted their scope to interpolated speeds, or generated unpredictable motions when extrapolating beyond trained velocity ranges. Variable-frequency imitation learning (VFIL) enabled extrapolations of speeds by linking the NN model's sampling frequency to the motion frequency, whereas its open-loop configuration caused frequency errors, especially in the extrapolated high-frequency settings. This study proposes variable-frequency imitation learning with iterative learning control (VFILC) based on a combination of VFIL and iterative learning control (ILC) with both feedforward and feedback parts, the former taking advantage of VFIL and the latter adjusting the frequency errors. The experimental results showed that the proposed method successfully and accurately extrapolated motion speeds and reduced frequency errors in all three tasks, and that the feedback especially reduced the frequency errors by a remarkable 81% in the wiping task and 50% in the shaking task, both compared to simple feedforward VFIL, when extrapolating at double the average speed in the training data. The proposed method also improved accuracy by 27% compared with VFIL even at an interpolated frequency for a contact-rich mixing task affected by complex friction traits.

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arXiv2026

Design and Experimental Validation of Sensorless 4-Channel Bilateral Teleoperation for Low-Cost Manipulators

Teleoperation of low-cost manipulators is attracting increasing attention as a practical means of collecting demonstration data for imitation learning. However, most existing low-cost systems rely on unilateral position control without force feedback, while implementing force-feedback bilateral teleoperation is difficult because low-cost manipulators typically have low-resolution encoders and no joint torque sensors. This paper presents a sensorless 4-channel bilateral teleoperation framework that integrates identified nonlinear dynamics compensation with a disturbance-observer-based velocity and external-force estimation scheme. By interpreting the observer structure in the frequency domain, we clarify the coupling between the velocity- and external-force-estimation bandwidths and derive practical tuning guidelines based on the damping ratio and a single cutoff frequency. Real-robot experiments, including force-sensor comparison and teleoperation tasks, demonstrate that the proposed framework provides practically useful force estimates and enables stable teleoperation in high-speed and contact-rich scenarios under low-cost hardware constraints. As an application, imitation-learning experiments demonstrate that incorporating estimated force information into demonstrations improves task success rates in the tested contact-rich manipulation tasks.

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arXiv2026

Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learning

Fast execution of contact-rich manipulation is critical for practical deployment, yet providing fast demonstrations for imitation learning (IL) remains challenging: humans cannot demonstrate at high speed, and naively accelerating demonstrations alters contact dynamics and induces large tracking errors. We present a method to autonomously refine time-accelerated demonstrations by repurposing Iterative Reference Learning Control (IRLC) to iteratively update the reference trajectory from observed tracking errors. However, applying IRLC directly at high speed tends to produce larger early-iteration errors and less stable transients. To address this issue, we propose Incremental Iterative Reference Learning Control (I2RLC), which gradually increases the speed while updating the reference, yielding high-fidelity trajectories. We validate on real-robot whiteboard erasing and peg-in-hole tasks using a teleoperation setup with a compliance-controlled follower and a 3D-printed haptic leader. Both IRLC and I2RLC achieve up to 10x faster demonstrations with reduced tracking error; moreover, I2RLC improves spatial similarity to the original trajectories by 22.5% on average over IRLC across three tasks and multiple speeds (3x-10x). We then use the refined trajectories to train IL policies; the resulting policies execute faster than the demonstrations and achieve 100% success rates in the peg-in-hole task at both seen and unseen positions, with I2RLC-trained policies exhibiting lower contact forces than those trained on IRLC-refined demonstrations. These results indicate that gradual speed scheduling coupled with reference adaptation provides a practical path to fast, contact-rich IL.

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arXiv2026

Force Generative Imitation Learning: Bridging Position Trajectory and Force Commands through Control Technique

In contact-rich tasks, while position trajectories are often easy to obtain, appropriate force commands are typically unknown. Although it is conceivable to generate force commands using a pretrained foundation model such as Vision-Language-Action (VLA) models, force control is highly dependent on the specific hardware of the robot, which makes the application of such models challenging. To bridge this gap, we propose a force generative model that estimates force commands from given position trajectories. However, when dealing with unseen position trajectories, the model struggles to generate accurate force commands. To address this, we introduce a feedback control mechanism. Our experiments reveal that feedback control does not converge when the force generative model has memory. We therefore adopt a model without memory, enabling stable feedback control. This approach allows the system to generate force commands effectively, even for unseen position trajectories, improving generalization for real-world robot writing tasks.

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arXiv2026

Hierarchical Proportion Models for Motion Generation via Integration of Motion Primitives

Imitation learning (IL) enables robots to acquire human-like motion skills from demonstrations, but it still requires extensive high-quality data and retraining to handle complex or long-horizon tasks. To improve data efficiency and adaptability, this study proposes a hierarchical IL framework that integrates motion primitives with proportion-based motion synthesis. The proposed method employs a two-layer architecture, where the upper layer performs long-term planning, while a set of lower-layer models learn individual motion primitives, which are combined according to specific proportions. Three model variants are introduced to explore different trade-offs between learning flexibility, computational cost, and adaptability: a learning-based proportion model, a sampling-based proportion model, and a playback-based proportion model, which differ in how the proportions are determined and whether the upper layer is trainable. Through real-robot pick-and-place experiments, the proposed models successfully generated complex motions not included in the primitive set. The sampling-based and playback-based proportion models achieved more stable and adaptable motion generation than the standard hierarchical model, demonstrating the effectiveness of proportion-based motion integration for practical robot learning.

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arXiv2025

Decoupled Scaling 4ch Bilateral Control on the Cartesian coordinate by 6-DoF Manipulator using Rotation Matrix

Four-channel bilateral control is a method for achieving remote control with force feedback and adjustment operability by synchronizing the positions and forces of two manipulators. This is expected to significantly improve the operability of the remote control in contact-rich tasks. Among these, 4-channel bilateral control on the Cartesian coordinate system is advantageous owing to its suitability for manipulators with different structures and because it allows the dynamics in the Cartesian coordinate system to be adjusted by adjusting the control parameters, thus achieving intuitive operability for humans. This paper proposes a 4-channel bilateral control method that achieves the desired dynamics by decoupling each dimension in the Cartesian coordinate system regardless of the scaling factor.

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arXiv2025

Imitation Learning Based on Disentangled Representation Learning of Behavioral Characteristics

In the field of robot learning, coordinating robot actions through language instructions is becoming increasingly feasible. However, adapting actions to human instructions remains challenging, as such instructions are often qualitative and require exploring behaviors that satisfy varying conditions. This paper proposes a motion generation model that adapts robot actions in response to modifier directives human instructions imposing behavioral conditions during task execution. The proposed method learns a mapping from modifier directives to actions by segmenting demonstrations into short sequences, assigning weakly supervised labels corresponding to specific modifier types. We evaluated our method in wiping and pick and place tasks. Results show that it can adjust motions online in response to modifier directives, unlike conventional batch-based methods that cannot adapt during execution.

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arXiv2025

Variable-Speed Teaching-Playback as Real-World Data Augmentation for Imitation Learning

Because imitation learning relies on human demonstrations in hard-to-simulate settings, the inclusion of force control in this method has resulted in a shortage of training data, even with a simple change in speed. Although the field of data augmentation has addressed the lack of data, conventional methods of data augmentation for robot manipulation are limited to simulation-based methods or downsampling for position control. This paper proposes a novel method of data augmentation that is applicable to force control and preserves the advantages of real-world datasets. We applied teaching-playback at variable speeds as real-world data augmentation to increase both the quantity and quality of environmental reactions at variable speeds. An experiment was conducted on bilateral control-based imitation learning using a method of imitation learning equipped with position-force control. We evaluated the effect of real-world data augmentation on two tasks, pick-and-place and wiping, at variable speeds, each from two human demonstrations at fixed speed. The results showed a maximum 55% increase in success rate from a simple change in speed of real-world reactions and improved accuracy along the duration/frequency command by gathering environmental reactions at variable speeds.

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arXiv2025

Motion Generation for Food Topping Challenge 2024: Serving Salmon Roe Bowl and Picking Fried Chicken

Although robots have been introduced in many industries, food production robots are yet to be widely employed because the food industry requires not only delicate movements to handle food but also complex movements that adapt to the environment. Force control is important for handling delicate objects such as food. In addition, achieving complex movements is possible by making robot motions based on human teachings. Four-channel bilateral control is proposed, which enables the simultaneous teaching of position and force information. Moreover, methods have been developed to reproduce motions obtained through human teachings and generate adaptive motions using learning. We demonstrated the effectiveness of these methods for food handling tasks in the Food Topping Challenge at the 2024 IEEE International Conference on Robotics and Automation (ICRA 2024). For the task of serving salmon roe on rice, we achieved the best performance because of the high reproducibility and quick motion of the proposed method. Further, for the task of picking fried chicken, we successfully picked the most pieces of fried chicken among all participating teams. This paper describes the implementation and performance of these methods.

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arXiv2025

Motion ReTouch: Motion Modification Using Four-Channel Bilateral Control

Recent research has demonstrated the usefulness of imitation learning in autonomous robot operation. In particular, teaching using four-channel bilateral control, which can obtain position and force information, has been proven effective. However, control performance that can easily execute high-speed, complex tasks in one go has not yet been achieved. We propose a method called Motion ReTouch, which retroactively modifies motion data obtained using four-channel bilateral control. The proposed method enables modification of not only position but also force information. This was achieved by the combination of multilateral control and motion-copying system. The proposed method was verified in experiments with a real robot, and the success rate of the test tube transfer task was improved, demonstrating the possibility of modification force information.

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arXiv2024

Error-Feedback Model for Output Correction in Bilateral Control-Based Imitation Learning

In recent years, imitation learning using neural networks has enabled robots to perform flexible tasks. However, since neural networks operate in a feedforward structure, they do not possess a mechanism to compensate for output errors. To address this limitation, we developed a feedback mechanism to correct these errors. By employing a hierarchical structure for neural networks comprising lower and upper layers, the lower layer was controlled to follow the upper layer. Additionally, using a multi-layer perceptron in the lower layer, which lacks an internal state, enhanced the error feedback. In the character-writing task, this model demonstrated improved accuracy in writing previously untrained characters. In the character-writing task, this model demonstrated improved accuracy in writing previously untrained characters. Through autonomous control with error feedback, we confirmed that the lower layer could effectively track the output of the upper layer. This study represents a promising step toward integrating neural networks with control theories.

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arXiv2024

Variable-Frequency Imitation Learning for Variable-Speed Motion

Conventional methods of imitation learning for variable-speed motion have difficulty extrapolating speeds because they rely on learning models running at a constant sampling frequency. This study proposes variable-frequency imitation learning (VFIL), a novel method for imitation learning with learning models trained to run at variable sampling frequencies along with the desired speeds of motion. The experimental results showed that the proposed method improved the velocity-wise accuracy along both the interpolated and extrapolated frequency labels, in addition to a 12.5 % increase in the overall success rate.

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arXiv2024

Loss Function Considering Dead Zone for Neural Networks

It is important to reveal the inverse dynamics of manipulators to improve control performance of model-based control. Neural networks (NNs) are promising techniques to represent complicated inverse dynamics while they require a large amount of motion data. However, motion data in dead zones of actuators is not suitable for training models decreasing the number of useful training data. In this study, based on the fact that the manipulator joint does not work irrespective of input torque in dead zones, we propose a new loss function that considers only errors of joints not in dead zones. The proposed method enables to increase in the amount of motion data available for training and the accuracy of the inverse dynamics computation. Experiments on actual equipment using a three-degree-of-freedom (DOF) manipulator showed higher accuracy than conventional methods. We also confirmed and discussed the behavior of the model of the proposed method in dead zones.

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arXiv2024

Imitation Learning Inputting Image Feature to Each Layer of Neural Network

Imitation learning enables robots to learn and replicate human behavior from training data. Recent advances in machine learning enable end-to-end learning approaches that directly process high-dimensional observation data, such as images. However, these approaches face a critical challenge when processing data from multiple modalities, inadvertently ignoring data with a lower correlation to the desired output, especially when using short sampling periods. This paper presents a useful method to address this challenge, which amplifies the influence of data with a relatively low correlation to the output by inputting the data into each neural network layer. The proposed approach effectively incorporates diverse data sources into the learning process. Through experiments using a simple pick-and-place operation with raw images and joint information as input, significant improvements in success rates are demonstrated even when dealing with data from short sampling periods.

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arXiv2023

Soft and Rigid Object Grasping With Cross-Structure Hand Using Bilateral Control-Based Imitation Learning

Object grasping is an important ability required for various robot tasks. In particular, tasks that require precise force adjustments during operation, such as grasping an unknown object or using a grasped tool, are difficult for humans to program in advance. Recently, AI-based algorithms that can imitate human force skills have been actively explored as a solution. In particular, bilateral control-based imitation learning achieves human-level motion speeds with environmental adaptability, only requiring human demonstration and without programming. However, owing to hardware limitations, its grasping performance remains limited, and tasks that involves grasping various objects are yet to be achieved. Here, we developed a cross-structure hand to grasp various objects. We experimentally demonstrated that the integration of bilateral control-based imitation learning and the cross-structure hand is effective for grasping various objects and harnessing tools.

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arXiv2022

Design of non-diagonal stiffness matrix for assembly task

Compliance control is an increasingly employed technique used in the robotic field. It is known that various mechanical properties can be reproduced depending on the design of the stiffness matrix, but the design theory that takes advantage of this high degree of design freedom has not been elucidated. This paper, therefore, discusses the non-diagonal elements of the stiffness matrix. We proposed a design method according to the conditions required for achieving stable motion. Additionally, we analyzed the displacement induced by the non-diagonal elements in response to an external force and found that to obtain stable contact with a symmetric matrix, the matrix should be positive definite, i.e., all eigenvalues must be positive, however its parameter design is complicated. In this study, we focused on the use of asymmetric matrices in compliance control and showed that the design of eigenvalues can be simplified by using a triangular matrix. This approach expands the range of the stiffness design and enhances the ability of the compliance control to induce motion. We conducted experiments using the stiffness matrix and confirmed that assembly could be achieved without complicated trajectory planning.

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arXiv2022

Imitation Learning for Nonprehensile Manipulation through Self-Supervised Learning Considering Motion Speed

Robots are expected to replace menial tasks such as housework. Some of these tasks include nonprehensile manipulation performed without grasping objects. Nonprehensile manipulation is very difficult because it requires considering the dynamics of environments and objects. Therefore imitating complex behaviors requires a large number of human demonstrations. In this study, a self-supervised learning that considers dynamics to achieve variable speed for nonprehensile manipulation is proposed. The proposed method collects and fine-tunes only successful action data obtained during autonomous operations. By fine-tuning the successful data, the robot learns the dynamics among itself, its environment, and objects. We experimented with the task of scooping and transporting pancakes using the neural network model trained on 24 human-collected training data. The proposed method significantly improved the success rate from 40.2% to 85.7%, and succeeded the task more than 75% for other objects.

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