62 citations · 165 across the 28 of their papers we have counts for
29 papers
Force Generative Imitation Learning: Bridging Position Trajectory and Force Commands through Control Technique
Hiroshi Sato, Sho Sakaino, Toshiaki Tsuji
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…
Hierarchical Proportion Models for Motion Generation via Integration of Motion Primitives
Yu-Han Shu, Toshiaki Tsuji, Sho Sakaino
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 o…
Decoupled Scaling 4ch Bilateral Control on the Cartesian coordinate by 6-DoF Manipulator using Rotation Matrix
Koki Yamane, Sho Sakaino, Toshiaki Tsuji
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…
Imitation Learning Based on Disentangled Representation Learning of Behavioral Characteristics
Ryoga Oishi, Sho Sakaino, Toshiaki Tsuji
In the field of robot learning, coordinating robot actions through language instructions is becoming increasingly feasible. However, adapting actions to human instructions remains…
Design and Experimental Validation of Sensorless 4-Channel Bilateral Teleoperation for Low-Cost Manipulators
Koki Yamane, Yunhan Li, Masashi Konosu +4
Teleoperation of low-cost manipulators is attracting increasing attention as a practical means of collecting demonstration data for imitation learning. However, most existing low-c…
Motion ReTouch: Motion Modification Using Four-Channel Bilateral Control
Koki Inami, Sho Sakaino, Toshiaki Tsuji
Recent research has demonstrated the usefulness of imitation learning in autonomous robot operation. In particular, teaching using four-channel bilateral control, which can obtain…
Variable-Speed Teaching-Playback as Real-World Data Augmentation for Imitation Learning
Nozomu Masuya, Hiroshi Sato, Koki Yamane +3
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, e…
Variable-Frequency Imitation Learning for Variable-Speed Motion
Nozomu Masuya, Sho Sakaino, Toshiaki Tsuji
Conventional methods of imitation learning for variable-speed motion have difficulty extrapolating speeds because they rely on learning models running at a constant sampling freque…
Error-Feedback Model for Output Correction in Bilateral Control-Based Imitation Learning
Hiroshi Sato, Masashi Konosu, Sho Sakaino +1
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 n…
Imitation Learning Inputting Image Feature to Each Layer of Neural Network
Koki Yamane, Sho Sakaino, Toshiaki Tsuji
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…
Soft and Rigid Object Grasping With Cross-Structure Hand Using Bilateral Control-Based Imitation Learning
Koki Yamane, Sho Sakaino, Toshiaki Tsuji
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…
Stability analysis of admittance control using asymmetric stiffness matrix
Toshiaki Tsuji, Yasuhiro Kato
In contact-rich tasks, setting the stiffness of the control system is a critical factor in its performance. Although the setting range can be extended by making the stiffness matri…
Design of non-diagonal stiffness matrix for assembly task
Tsukasa Kusakabe, Sho Sakaino, Toshiaki Tsuji
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…
Imitation Learning for Nonprehensile Manipulation through Self-Supervised Learning Considering Motion Speed
Yuki Saigusa, Sho Sakaino, Toshiaki Tsuji
Robots are expected to replace menial tasks such as housework. Some of these tasks include nonprehensile manipulation performed without grasping objects. Nonprehensile manipulation…
An Independently Learnable Hierarchical Model for Bilateral Control-Based Imitation Learning Applications
Kazuki Hayashi, Sho Sakaino, Toshiaki Tsuji
Recently, motion generation by machine learning has been actively researched to automate various tasks. Imitation learning is one such method that learns motions from data collecte…
A Self-Tuning Impedance-based Interaction Planner for Robotic Haptic Exploration
Yasuhiro Kato, Pietro Balatti, Juan M. Gandarias +3
This paper presents a novel interaction planning method that exploits impedance tuning techniques in response to environmental uncertainties and unpredictable conditions using hapt…
Force control of grinding process based on frequency analysis
Yuya Nogi, Sho Sakaino, Toshiaki Tsuji
Hysteresis-induced drift is a major issue in the detection of force induced during grinding and cutting operations. In this paper, we propose an external force estimation method ba…
Generation Drawing/Grinding Trajectoy Based on Hierarchical CVAE
Masahiro Aita, Keito Sugawara, Sho Sakaino +1
In this study, we propose a method to model the local and global features of the drawing/grinding trajectory with hierarchical Variational Autoencoders (VAEs). By combining two sep…
A New Autoregressive Neural Network Model with Command Compensation for Imitation Learning Based on Bilateral Control
Kazuki Hayashi, Ayumu Sasagawa, Sho Sakaino +1
In the near future, robots are expected to work with humans or operate alone and may replace human workers in various fields such as homes and factories. In a previous study, we pr…
Imitation learning for variable speed motion generation over multiple actions
Yuki Saigusa, Ayumu Sasagawa, Sho Sakaino +1
Robotic motion generation methods using machine learning have been studied in recent years. Bilateral control-based imitation learning can imitate human motions using force informa…
Imitation Learning for Variable Speed Contact Motion for Operation up to Control Bandwidth
Sho Sakaino, Kazuki Fujimoto, Yuki Saigusa +1
The generation of robot motions in the real world is difficult by using conventional controllers alone and requires highly intelligent processing. In this regard, learning-based mo…
Motion Generation Using Bilateral Control-Based Imitation Learning with Autoregressive Learning
Ayumu Sasagawa, Sho Sakaino, Toshiaki Tsuji
Robots that can execute various tasks automatically on behalf of humans are becoming an increasingly important focus of research in the field of robotics. Imitation learning has be…
Assembly robots with optimized control stiffness through reinforcement learning
Masahide Oikawa, Kyo Kutsuzawa, Sho Sakaino +1
There is an increased demand for task automation in robots. Contact-rich tasks, wherein multiple contact transitions occur in a series of operations, are extensively being studied…
Design of Resonance Ratio Control with Relative Position Information for Two-inertia System
Kenta Araake, Sho Sakaino, Toshiaki Tsuji
Two-inertia systems are prone to resonance vibrations that degrade their control performances. These unwanted vibrations can be effectively suppressed by control methods based on a…