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Shuhei Ikemoto

15 accepted papers

2025

Real-Time Shape Estimation of Tensegrity Structures Using Strut Inclination Angles

RA-L 2025

Tensegrity structures are becoming widely used in robotics, such as continuously bending soft manipulators and mobile robots to explore unknown and uneven environments dynamically. Estimating their shape, which is the foundation of their state, is essential for establishing control. However, on-boar

Cited by 1SourceScholar
2025

Uncertainty-aware Motion Planning based on Stochastic Forward/Inverse Kinematics Models for Tensegrity Manipulators

IROS 2025

Robots whose shape and stiffness are determined by internal forces generally have complex shape-stiffness relationships that depend on their structure. As a result, there are difficulties such as a decrease in shape reproducibility when the robot is not stiff, and a decrease in the range of motion w

Cited by 0SourceScholar
2024

Active Learning for Forward/Inverse Kinematics of Redundantly-driven Flexible Tensegrity Manipulator

IROS 2024poster

In flexible redundantly-driven multi-DOF systems, like living beings, the representation of redundant kinematics including the diversity of solutions, is crucial for leveraging its distinctive characteristics. This paper proposes an active learning framework for forward and inverse modeling of compl…

Cited by 0SourceScholar
2024

Diff-Control: A Stateful Diffusion-based Policy for Imitation Learning

IROS 2024poster

While imitation learning provides a simple and effective framework for policy learning, acquiring consistent action during robot execution remains a challenging task. Existing approaches primarily focus on either modifying the action representation at data curation stage or altering the model itself…

Cited by 2SourcecodeScholar
2023

$\alpha$-MDF: An Attention-based Multimodal Differentiable Filter for Robot State Estimation

CoRL 2023poster

Differentiable Filters are recursive Bayesian estimators that derive the state transition and measurement models from data alone. Their data-driven nature eschews the need for explicit analytical models, while remaining algorithmic components of the filtering process intact. As a result, the gain me…

Cited by 8SourcecodeScholar
2023

Forward/Inverse Kinematics Modeling for Tensegrity Manipulator Based on Goal-Conditioned Variational Autoencoder

IROS 2023poster

This paper uses a data-driven approach to model a highly redundantly driven tensegrity manipulator's forward and inverse kinematics. The tensegrity manipulator is based on a class-1 tensegrity with 20 struts and bends by 40 pneumatic actuators whose internal pressures are independently controlled. B…

Cited by 4SourceScholar
2023

Learning Soft Robot Dynamics Using Differentiable Kalman Filters and Spatio-Temporal Embeddings

IROS 2023poster

This paper introduces a novel approach for modeling the dynamics of soft robots, utilizing a differentiable filter architecture. The proposed approach enables end-to-end training to learn system dynamics, noise characteristics, and temporal behavior of the robot. A novel spatio-temporal embedding pr…

Cited by 7SourcecodeScholar
2022

Development of Pneumatically Driven Tensegrity Manipulator without Mechanical Springs

IROS 2022poster

This paper reports a tensegrity manipulator driven by 40 pneumatic cylinders without mechanical springs. In general, tensegrity robots use mechanical springs to achieve a stable/curved tensegrity structure, and this is true even when a component extends/retracts with an actuator. The stiffness of th…

Cited by 12SourceScholar
2019

Common Dimensional Autoencoder for Learning Redundant Muscle-Posture Mappings of Complex Musculoskeletal Robots

IROS 2019poster

It has been widely considered that a distinctive feature of musculoskeletal structures is that both the joint angle and stiffness can be changed by exploiting the agonistantagonist driving of the joint. However, musculoskeletal systems in animals and humans are typically highly complex, and the simp…

Cited by 7SourceScholar
2019

Learning Interactive Behaviors for Musculoskeletal Robots Using Bayesian Interaction Primitives

IROS 2019poster

Musculoskeletal robots that are based on pneumatic actuation have a variety of properties, such as compliance and back-drivability, that render them particularly appealing for human-robot collaboration. However, programming interactive and responsive behaviors for such systems is extremely challengi…

Cited by 21SourceScholar
2019

Local Online Motor Babbling: Learning Motor Abundance of a Musculoskeletal Robot Arm

IROS 2019poster

Motor babbling and goal babbling has been used for sensorimotor learning of highly redundant systems in soft robotics. Recent works in goal babbling have demonstrated successful learning of inverse kinematics (IK) on such systems, and suggest that babbling in the goal space better resolves motor red…

Cited by 4SourceScholar
2018

Optimal Feedback Control Based on Analytical Linear Models Extracted from Neural Networks Trained for Nonlinear Systems

IROS 2018poster

A number of researches have been focusing on the development and control of robots with soft structures such as flexible musculoskeletal systems. Thus far, it has been reported that these robots can achieve high adaptability to environments despite their extremely simple controllers. However, becaus…

Cited by 3SourceScholar
2015

Understanding function of gluteus medius in human walking from constructivist approach

IROS 2015poster

Humans can walk stably and adaptively in the presence of various environmental changes. Their bodies have very complex musculoskeletal structures that contribute to their walking stability and adaptability. In this paper, we focus on the gluteus medius, one of muscles contributing to the support of…

Cited by 5SourceScholar