Selected work

Representative Papers

SPDAlign: Interpretable Riemannian Alignment for EEG Forward Modeling Shifts

Oct 05, 2026

This study addresses the cross-domain distribution shift caused by the non-stationarity of EEG signals by proposing an unsupervised domain adaptation framework grounded in Riemannian geometry. We theoretically prove that forward modeling shifts can be fully recovered via linear transformations on the symmetric positive definite (SPD) manifold, thereby establishing a globally linear and intrinsically interpretable alignment mechanism. Furthermore, Wasserstein Procrustes optimal transport is introduced to jointly align means and correct rotational discrepancies. Experimental evaluations on both simulated and public EEG datasets demonstrate that the proposed method achieves superior performance, accurately identifies critical frequency bands and spatial patterns, and effectively overcomes inter-subject variability.

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OpenRoIS: A Community-Driven Open-Source Middleware Implementing the Robotic Interaction Service (RoIS) Framework for Physical Robots and Virtual Agents

Sep 17, 2026

"This study addresses the challenge of human-robot interaction (HRI) services being tightly coupled with specific hardware, necessitating reimplementation when transitioning between different platforms. The work proposes RoIS 2.0, an open-source middleware that standardizes the interface between service applications and HRI engines, thereby enabling cross-platform compatibility. Key innovations include a single engine class for both primary and secondary HRI roles, a five-method component contract, the use of JSON-RPC 2.0 over WebSocket for interface mapping, and a unified type pipeline that supports TypeScript, C#, and Python. Additionally, an adapter for ROS 2 is provided, allowing services to interact with both physical robots and virtual agents via the internet. Released under the Apache-2.0 license, RoIS 2.0 is an ongoing development project."

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Rectifying Geometric Misalignment: Online Source-Free Adaptation for Class-Imbalanced EEG

Aug 05, 2026

This work addresses the degradation of Riemannian manifold geometric alignment in online brain–computer interfaces caused by label shift. To tackle this issue without access to source-domain data, the authors propose the Online Streaming Plug-and-Play Domain-Invariant Manifold (OSPDIM) framework, which introduces manifold-constrained bias into tangent space mapping for the first time. By integrating an online information maximization criterion, OSPDIM adaptively optimizes bias parameters in real time, enabling plug-and-play geometric correction without reliance on historical batch statistics. Experimental results demonstrate that OSPDIM significantly outperforms standard Riemannian methods across multiple motor imagery EEG datasets, exhibiting exceptional robustness—particularly under severe class imbalance in online settings.

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SHAPE: Simultaneous Water Hydraulic Actuation and Position Estimation of a Sensorless Remote Actuator through a Thin and Long Flexible Tube

Jul 18, 2026

This work addresses the challenge of precise position control in long, flexible, low-impedance hydraulic actuation systems operating in harsh environments, where conventional robots suffer from sensor fragility and the impracticality of embedding sensors. The authors propose an innovative approach that simultaneously transmits actuation power and state information through a single water-filled flexible tube. By modeling volume loss due to pressure-induced tube deformation and the subtle effects of entrained air, they achieve high-accuracy, sensorless position estimation and actuation over tube lengths up to 50 meters. Key contributions include the first demonstration of integrated drive and position feedback using only a single hydraulic line without end-effector sensors, and a practical online parameter identification method to compensate for inter-tube variability and air content fluctuations. Experiments validate stable position control of hydraulic cylinders under varying loads, establishing a robust framework for remote robotic operation in extreme conditions.

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Actuator Reality Shaping for Zero-Shot Sim-to-Real Robot Learning

Jul 02, 2026

This work addresses the sim-to-real transfer failure commonly encountered in reinforcement learning due to mismatches between idealized actuator models used in simulation and the nonlinear, hardware-dependent motor dynamics of real robots. To bridge this gap, the authors propose “actuator reality shaping,” a method that deploys a two-degree-of-freedom feedforward–feedback controller on physical hardware to shape the closed-loop actuator response to closely match an ideal second-order reference model assumed in simulation. Notably, this approach requires no system identification or learned actuator models and enables zero-shot policy deployment through a standardized actuator interface. Experiments across diverse platforms—including single-joint servos, a 7-DoF manipulator, wheeled-legged robots, and humanoids—demonstrate substantial reductions in tracking error and successful zero-shot transfer across multiple tasks and systems, thereby shifting the paradigm from increasing simulation fidelity to unifying real-world actuator behavior to conform to simulation assumptions.

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Recent publications

Latest Papers

SPDAlign: Interpretable Riemannian Alignment for EEG Forward Modeling Shifts

Oct 05, 2026

This study addresses the cross-domain distribution shift caused by the non-stationarity of EEG signals by proposing an unsupervised domain adaptation framework grounded in Riemannian geometry. We theoretically prove that forward modeling shifts can be fully recovered via linear transformations on the symmetric positive definite (SPD) manifold, thereby establishing a globally linear and intrinsically interpretable alignment mechanism. Furthermore, Wasserstein Procrustes optimal transport is introduced to jointly align means and correct rotational discrepancies. Experimental evaluations on both simulated and public EEG datasets demonstrate that the proposed method achieves superior performance, accurately identifies critical frequency bands and spatial patterns, and effectively overcomes inter-subject variability.

0 citationsRead paper

OpenRoIS: A Community-Driven Open-Source Middleware Implementing the Robotic Interaction Service (RoIS) Framework for Physical Robots and Virtual Agents

Sep 17, 2026

"This study addresses the challenge of human-robot interaction (HRI) services being tightly coupled with specific hardware, necessitating reimplementation when transitioning between different platforms. The work proposes RoIS 2.0, an open-source middleware that standardizes the interface between service applications and HRI engines, thereby enabling cross-platform compatibility. Key innovations include a single engine class for both primary and secondary HRI roles, a five-method component contract, the use of JSON-RPC 2.0 over WebSocket for interface mapping, and a unified type pipeline that supports TypeScript, C#, and Python. Additionally, an adapter for ROS 2 is provided, allowing services to interact with both physical robots and virtual agents via the internet. Released under the Apache-2.0 license, RoIS 2.0 is an ongoing development project."

0 citationsRead paper

Rectifying Geometric Misalignment: Online Source-Free Adaptation for Class-Imbalanced EEG

Aug 05, 2026

This work addresses the degradation of Riemannian manifold geometric alignment in online brain–computer interfaces caused by label shift. To tackle this issue without access to source-domain data, the authors propose the Online Streaming Plug-and-Play Domain-Invariant Manifold (OSPDIM) framework, which introduces manifold-constrained bias into tangent space mapping for the first time. By integrating an online information maximization criterion, OSPDIM adaptively optimizes bias parameters in real time, enabling plug-and-play geometric correction without reliance on historical batch statistics. Experimental results demonstrate that OSPDIM significantly outperforms standard Riemannian methods across multiple motor imagery EEG datasets, exhibiting exceptional robustness—particularly under severe class imbalance in online settings.

0 citationsRead paper

SHAPE: Simultaneous Water Hydraulic Actuation and Position Estimation of a Sensorless Remote Actuator through a Thin and Long Flexible Tube

Jul 18, 2026

This work addresses the challenge of precise position control in long, flexible, low-impedance hydraulic actuation systems operating in harsh environments, where conventional robots suffer from sensor fragility and the impracticality of embedding sensors. The authors propose an innovative approach that simultaneously transmits actuation power and state information through a single water-filled flexible tube. By modeling volume loss due to pressure-induced tube deformation and the subtle effects of entrained air, they achieve high-accuracy, sensorless position estimation and actuation over tube lengths up to 50 meters. Key contributions include the first demonstration of integrated drive and position feedback using only a single hydraulic line without end-effector sensors, and a practical online parameter identification method to compensate for inter-tube variability and air content fluctuations. Experiments validate stable position control of hydraulic cylinders under varying loads, establishing a robust framework for remote robotic operation in extreme conditions.

0 citationsRead paper

Actuator Reality Shaping for Zero-Shot Sim-to-Real Robot Learning

Jul 02, 2026

This work addresses the sim-to-real transfer failure commonly encountered in reinforcement learning due to mismatches between idealized actuator models used in simulation and the nonlinear, hardware-dependent motor dynamics of real robots. To bridge this gap, the authors propose “actuator reality shaping,” a method that deploys a two-degree-of-freedom feedforward–feedback controller on physical hardware to shape the closed-loop actuator response to closely match an ideal second-order reference model assumed in simulation. Notably, this approach requires no system identification or learned actuator models and enables zero-shot policy deployment through a standardized actuator interface. Experiments across diverse platforms—including single-joint servos, a 7-DoF manipulator, wheeled-legged robots, and humanoids—demonstrate substantial reductions in tracking error and successful zero-shot transfer across multiple tasks and systems, thereby shifting the paradigm from increasing simulation fidelity to unifying real-world actuator behavior to conform to simulation assumptions.

0 citationsRead paper