arXiv subjects

Jaeheung Park

Publications and source records attributed to Jaeheung Park.

At least 19 recordsLinked to original sources

arXiv2026

Extreme, transient bursts of energy in the auroral ionosphere. II. A magnetotail dipolarization event

We report ground-based coherent VHF radar observations of extreme turbulent field-structures detected in coincidence with a magnetospheric substorm-associated magnetotail dipolarization. The field-structures are observed by the ICEBEAR radar, in the form of Farley-Buneman (FB) waves in the auroral electrojets, and the field-structures themselves move an order of magnitude faster than the saturation speed of the underlying FB waves, implying transient electric field sources up to 330 mV/m in strength. The field-structures are identified and automatically tracked using an unsupervised clustering & tracking algorithm, applied to clutters of ICEBEAR radar backscatter targets, a method that turns the Doppler radar into a tracking radar capable of measuring the ionospheric ExB-drift by proxy. We place this finding in a coordinated multi-instrument context. Three THEMIS spacecraft observed the dipolarization event in-situ in the near-Earth plasma sheet. In the ionosphere, Swarm A, crossing through the guilty auroral arc at the onset of the dipolarization event, recorded clear signatures of propagating Alfvén waves threading the relevant flux tube. We interpret the ICEBEAR transients as the natural ionospheric foot signature of a shear Alfvén pulse launched by the bipolar space-charge (Hall) electric field of the thinned current sheet, with amplification along the converging flux tube, partial reflection at the ionospheric boundary, and spatial sharpening by precipitation-produced Pedersen-conductance gradients on the auroral arc edges. A one-dimensional wave-transmission analysis recovers the observations. Our results elucidate a tightly controlled coupling between magnetotail processes and meter-scale auroral plasma turbulence, and demonstrate the capability of ICEBEAR to resolve extreme, transient electric-field enhancements in the ionosphere.

physics.space-ph↗

arXiv2026

Safety-Critical Whole-Body Control for Humanoid Robots via Input-to-State Safe Control Barrier Functions

Safety-critical control is essential for humanoid robots operating in complex human-centered environments, where physical safety constraints such as joint limits, self-collision avoidance, obstacle avoidance, and workspace boundaries must be satisfied during real-robot operation. However, existing approaches remain limited because kinematic safety guarantees can be degraded in the presence of unknown disturbances, such as model uncertainties, trajectory-tracking errors, and external perturbations. This paper presents a hierarchical safety-critical whole-body control framework for humanoid robots based on input-to-state safe control barrier functions (ISSf-CBFs). The proposed architecture integrates a kinematic-level whole-body controller (KinWBC), an ISSf-CBF safety filter, and a dynamic-level whole-body controller (DynWBC). KinWBC generates nominal joint-motion references from prioritized tasks; the ISSf-CBF filter minimally modifies these references to satisfy kinematic safety constraints under bounded disturbances; and DynWBC tracks the filtered references while enforcing full-body dynamic feasibility and contact stability. Safety constraints are imposed on a whole-body kinematic model, and the ISSf-CBF parameters are conservatively tuned so that the resulting kinematic safety guarantees can be transferred to full-order humanoid dynamics under unknown disturbances. Simulation and real-robot experiments demonstrate that the proposed framework improves safety margins under model mismatch and reliably enforces multiple safety constraints in real time during locomotion, teleoperation, and single-leg balancing with hand control. Project website: https://kwlee365.github.io/SafeWBC-Website/

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arXiv2026

MVAdapt: Zero-Shot Multi-Vehicle Adaptation for End-to-End Autonomous Driving

End-to-End (E2E) autonomous driving models are usually trained and evaluated with a fixed ego-vehicle, even though their driving policy is implicitly tied to vehicle dynamics. When such a model is deployed on a vehicle with different size, mass, or drivetrain characteristics, its performance can degrade substantially; we refer to this problem as the vehicle-domain gap. To address it, we propose MVAdapt, a physics-conditioned adaptation framework for multi-vehicle E2E driving. MVAdapt combines a frozen TransFuser++ scene encoder with a lightweight physics encoder and a cross-attention module that conditions scene features on vehicle properties before waypoint decoding. In the CARLA Leaderboard 1.0 benchmark, MVAdapt improves over naive transfer and multi-embodiment adaptation baselines on both in-distribution and unseen vehicles. We further show two complementary behaviors: strong zero-shot transfer on many unseen vehicles, and data-efficient few-shot calibration for severe physical outliers. These results suggest that explicitly conditioning E2E driving policies on vehicle physics is an effective step toward more transferable autonomous driving models. All codes are available at https://github.com/hae-sung-oh/MVAdapt

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arXiv2026

Shoulder Range of Motion Rehabilitation Robot Incorporating Scapulohumeral Rhythm for Frozen Shoulder

This paper presents a novel rehabilitation robot designed to address the challenges of Passive Range of Motion (PROM) exercises for frozen shoulder patients by integrating advanced scapulohumeral rhythm stabilization. Frozen shoulder is characterized by limited glenohumeral motion and disrupted scapulohumeral rhythm, with therapist-assisted interventions being highly effective for restoring normal shoulder function. While existing robotic solutions replicate natural shoulder biomechanics, they lack the ability to stabilize compensatory movements, such as shoulder shrugging, which are critical for effective rehabilitation. Our proposed device features a 6 Degrees of Freedom (DoF) mechanism, including 5 DoF for shoulder motion and an innovative 1 DoF Joint press for scapular stabilization. The robot employs a personalized two-phase operation: recording normal shoulder movement patterns from the unaffected side and applying them to guide the affected side. Experimental results demonstrated the robot's ability to replicate recorded motion patterns with high precision, with Root Mean Square Error (RMSE) values consistently below 1 degree. In simulated frozen shoulder conditions, the robot effectively suppressed scapular elevation, delaying the onset of compensatory movements and guiding the affected shoulder to move more closely in alignment with normal shoulder motion, particularly during arm elevation movements such as abduction and flexion. These findings confirm the robot's potential as a rehabilitation tool capable of automating PROM exercises while correcting compensatory movements. The system provides a foundation for advanced, personalized rehabilitation for patients with frozen shoulders.

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arXiv2026

Connectivity-Aware Representations for Constrained Motion Planning via Multi-Scale Contrastive Learning

The objective of constrained motion planning is to connect start and goal configurations while satisfying task-specific constraints. Motion planning becomes inefficient or infeasible when the configurations lie in disconnected regions, known as essentially mutually disconnected (EMD) components. Constraints further restrict feasible space to a lower-dimensional submanifold, while redundancy introduces additional complexity because a single end-effector pose admits infinitely many inverse kinematic solutions that may form discrete self-motion manifolds. This paper addresses these challenges by learning a connectivity-aware representation for selecting start and goal configurations prior to planning. Joint configurations are embedded into a latent space through multi-scale manifold learning across neighborhood ranges from local to global, and clustering generates pseudo-labels that supervise a contrastive learning framework. The proposed framework provides a connectivity-aware measure that biases the selection of start and goal configurations in connected regions, avoiding EMDs and yielding higher success rates with reduced planning time. Experiments on various manipulation tasks showed that our method achieves 1.9 times higher success rates and reduces the planning time by a factor of 0.43 compared to baselines.

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arXiv2026

Sim-to-Real of Humanoid Locomotion Policies via Joint Torque Space Perturbation Injection

This paper proposes a novel alternative to existing sim-to-real methods for training control policies with simulated experiences. Prior sim-to-real methods for legged robots mostly rely on the domain randomization approach, where a fixed finite set of simulation parameters is randomized during training. Instead, our method adds state-dependent perturbations to the input joint torque used for forward simulation during the training phase. These state-dependent perturbations are designed to simulate a broader range of reality gaps than those captured by randomizing a fixed set of simulation parameters. Experimental results show that our method enables humanoid locomotion policies that achieve greater robustness against complex reality gaps unseen in the training domain.

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arXiv2026

Sim-to-Real of Humanoid Locomotion Policies via Joint Torque Space Perturbation Injection

This paper proposes a novel alternative to existing sim-to-real methods for training control policies with simulated experiences. Unlike prior methods that typically rely on domain randomization over a fixed finite set of parameters, the proposed approach injects state-dependent perturbations into the input joint torque during forward simulation. These perturbations are designed to simulate a broader spectrum of reality gaps than standard parameter randomization without requiring additional training. By using neural networks as flexible perturbation generators, the proposed method can represent complex, state-dependent uncertainties, such as nonlinear actuator dynamics and contact compliance, that parametric randomization cannot capture. Experimental results demonstrate that the proposed approach enables humanoid locomotion policies to achieve superior robustness against complex, unseen reality gaps in both simulation and real-world deployment.

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arXiv2026

TOLEBI: Learning Fault-Tolerant Bipedal Locomotion via Online Status Estimation and Fallibility Rewards

With the growing employment of learning algorithms in robotic applications, research on reinforcement learning for bipedal locomotion has become a central topic for humanoid robotics. While recently published contributions achieve high success rates in locomotion tasks, scarce attention has been devoted to the development of methods that enable to handle hardware faults that may occur during the locomotion process. However, in real-world settings, environmental disturbances or sudden occurrences of hardware faults might yield severe consequences. To address these issues, this paper presents TOLEBI (A faulT-tOlerant Learning framEwork for Bipedal locomotIon) that handles faults on the robot during operation. Specifically, joint locking, power loss and external disturbances are injected in simulation to learn fault-tolerant locomotion strategies. In addition to transferring the learned policy to the real robot via sim-to-real transfer, an online joint status module incorporated. This module enables to classify joint conditions by referring to the actual observations at runtime under real-world conditions. The validation experiments conducted both in real-world and simulation with the humanoid robot TOCABI highlight the applicability of the proposed approach. To our knowledge, this manuscript provides the first learning-based fault-tolerant framework for bipedal locomotion, thereby fostering the development of efficient learning methods in this field.

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arXiv2026

Phase-based Nonlinear Model Predictive Control for Humanoid Walking Stabilization with Single and Double Support Time Adjustments

The contact sequence of humanoid walking consists of single and double support phases (SSP and DSP), and their coordination through proper duration and dynamic transition based on the robot's state is crucial for maintaining walking stability. Numerous studies have investigated phase duration optimization as an effective means of improving walking stability. This paper presents a phase-based Nonlinear Model Predictive Control (NMPC) framework that jointly optimizes Zero Moment Point (ZMP) modulation, step location, SSP duration (step timing), and DSP duration within a single formulation. Specifically, the proposed framework reformulates the nonlinear DCM (Divergent Component of Motion) error dynamics into a phase-consistent representation and incorporates them as dynamic constraints within the NMPC. The proposed framework also guarantees ZMP input continuity during contact-phase transitions and disables footstep updates during the DSP, thereby enabling dynamically reliable balancing control regardless of whether the robot is in SSP or DSP. The effectiveness of the proposed method is validated through extensive simulation and hardware experiments, demonstrating improved balance performance under external disturbances.

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arXiv2025

GUARD: Toward a Compromise between Traditional Control and Learning for Safe Robot Systems

This paper presents the framework \textbf{GUARD} (\textbf{G}uided robot control via \textbf{U}ncertainty attribution and prob\textbf{A}bilistic kernel optimization for \textbf{R}isk-aware \textbf{D}ecision making) that combines traditional control with an uncertainty-aware perception technique using active learning with real-time capability for safe robot collision avoidance. By doing so, this manuscript addresses the central challenge in robotics of finding a reasonable compromise between traditional methods and learning algorithms to foster the development of safe, yet efficient and flexible applications. By unifying a reactive model predictive countouring control (RMPCC) with an Iterative Closest Point (ICP) algorithm that enables the attribution of uncertainty sources online using active learning with real-time capability via a probabilistic kernel optimization technique, \emph{GUARD} inherently handles the existing ambiguity of the term \textit{safety} that exists in robotics literature. Experimental studies indicate the high performance of \emph{GUARD}, thereby highlighting the relevance and need to broaden its applicability in future.

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arXiv2025

Reactive Model Predictive Contouring Control for Robot Manipulators

This contribution presents a robot path-following framework via Reactive Model Predictive Contouring Control (RMPCC) that successfully avoids obstacles, singularities and self-collisions in dynamic environments at 100 Hz. Many path-following methods rely on the time parametrization, but struggle to handle collision and singularity avoidance while adhering kinematic limits or other constraints. Specifically, the error between the desired path and the actual position can become large when executing evasive maneuvers. Thus, this paper derives a method that parametrizes the reference path by a path parameter and performs the optimization via RMPCC. In particular, Control Barrier Functions (CBFs) are introduced to avoid collisions and singularities in dynamic environments. A Jacobian-based linearization and Gauss-Newton Hessian approximation enable solving the nonlinear RMPCC problem at 100 Hz, outperforming state-of-the-art methods by a factor of 10. Experiments confirm that the framework handles dynamic obstacles in real-world settings with low contouring error and low robot acceleration.

cs.RO↗

arXiv2025

Meter-scale Observations of Equatorial Plasma Turbulence

The multi-Needle Langmuir Probe collects an electron current through four fixed-bias cylindrical copper needles. This allows for an extremely high sampling frequency, with plasma properties being inferred through polynomial fitting in the current-voltage plane. We present initial results from such a multi-needle probe mounted on the International Space Station, orbiting Earth at an altitude of around 400 km. That altitude, and its orbital inclination (~50 degrees), place the ISS as a suitable platform for observing equatorial plasma bubbles. In case studies of such turbulent structuring of the F-region plasma, we observe density timeseries that conserve considerable detail at virtually every level of magnification down to its Nyquist scale of 2-5 meters. We present power spectral density estimates of the turbulent structuring found inside equatorial plasma bubbles, and we discuss apparent break-points at scale-sizes between 1 m and 300 m, which we interpret in the light of turbulent dissipation as kilometer-scale swirls produced by the gradient-drift instability dissipate in the plasma.

physics.space-ph↗

arXiv2025

Generating Diverse Challenging Terrains for Legged Robots Using Quality-Diversity Algorithm

While legged robots have achieved significant advancements in recent years, ensuring the robustness of their controllers on unstructured terrains remains challenging. It requires generating diverse and challenging unstructured terrains to test the robot and discover its vulnerabilities. This topic remains underexplored in the literature. This paper presents a Quality-Diversity framework to generate diverse and challenging terrains that uncover weaknesses in legged robot controllers. Our method, applied to both simulated bipedal and quadruped robots, produces an archive of terrains optimized to challenge the controller in different ways. Quantitative and qualitative analyses show that the generated archive effectively contains terrains that the robots struggled to traverse, presenting different failure modes. Interesting results were observed, including failure cases that were not necessarily expected. Experiments show that the generated terrains can also be used to improve RL-based controllers.

cs.RO↗

arXiv2025

Real-time Whole-body Model Predictive Control for Bipedal Locomotion with a Novel Kino-dynamic Model and Warm-start Method

Advancements in optimization solvers and computing power have led to growing interest in applying whole-body model predictive control (WB-MPC) to bipedal robots. However, the high degrees of freedom and inherent model complexity of bipedal robots pose significant challenges in achieving fast and stable control cycles for real-time performance. This paper introduces a novel kino-dynamic model and warm-start strategy for real-time WB-MPC in bipedal robots. Our proposed kino-dynamic model combines the linear inverted pendulum plus flywheel and full-body kinematics model. Unlike the conventional whole-body model that rely on the concept of contact wrenches, our model utilizes the zero-moment point (ZMP), reducing baseline computational costs and ensuring consistently low latency during contact state transitions. Additionally, a modularized multi-layer perceptron (MLP) based warm-start strategy is proposed, leveraging a lightweight neural network to provide a good initial guess for each control cycle. Furthermore, we present a ZMP-based whole-body controller (WBC) that extends the existing WBC for explicitly controlling impulses and ZMP, integrating it into the real-time WB-MPC framework. Through various comparative experiments, the proposed kino-dynamic model and warm-start strategy have been shown to outperform previous studies. Simulations and real robot experiments further validate that the proposed framework demonstrates robustness to perturbation and satisfies real-time control requirements during walking.

cs.RO↗

arXiv2025

A Model Predictive Capture Point Control Framework for Robust Humanoid Balancing via Ankle, Hip, and Stepping Strategies

The robust balancing capability of humanoids is essential for mobility in real environments. Many studies focus on implementing human-inspired ankle, hip, and stepping strategies to achieve human-level balance. In this paper, a robust balance control framework for humanoids is proposed. Firstly, a Model Predictive Control (MPC) framework is proposed for Capture Point (CP) tracking control, enabling the integration of ankle, hip, and stepping strategies within a single framework. Additionally, a variable weighting method is introduced that adjusts the weighting parameters of the Centroidal Angular Momentum damping control. Secondly, a hierarchical structure of the MPC and a stepping controller was proposed, allowing for the step time optimization. The robust balancing performance of the proposed method is validated through simulations and real robot experiments. Furthermore, a superior balancing performance is demonstrated compared to a state-of-the-art Quadratic Programming-based CP controller that employs the ankle, hip, and stepping strategies.

cs.RO↗

arXiv2025

Spectral Normalization for Lipschitz-Constrained Policies on Learning Humanoid Locomotion

Reinforcement learning (RL) has shown great potential in training agile and adaptable controllers for legged robots, enabling them to learn complex locomotion behaviors directly from experience. However, policies trained in simulation often fail to transfer to real-world robots due to unrealistic assumptions such as infinite actuator bandwidth and the absence of torque limits. These conditions allow policies to rely on abrupt, high-frequency torque changes, which are infeasible for real actuators with finite bandwidth. Traditional methods address this issue by penalizing aggressive motions through regularization rewards, such as joint velocities, accelerations, and energy consumption, but they require extensive hyperparameter tuning. Alternatively, Lipschitz-Constrained Policies (LCP) enforce finite bandwidth action control by penalizing policy gradients, but their reliance on gradient calculations introduces significant GPU memory overhead. To overcome this limitation, this work proposes Spectral Normalization (SN) as an efficient replacement for enforcing Lipschitz continuity. By constraining the spectral norm of network weights, SN effectively limits high-frequency policy fluctuations while significantly reducing GPU memory usage. Experimental evaluations in both simulation and real-world humanoid robot show that SN achieves performance comparable to gradient penalty methods while enabling more efficient parallel training.

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arXiv2025

MOB-Net: Limb-modularized Uncertainty Torque Learning of Humanoids for Sensorless External Torque Estimation

Momentum observer (MOB) can estimate external joint torque without requiring additional sensors, such as force/torque or joint torque sensors. However, the estimation performance of MOB deteriorates due to the model uncertainty which encompasses the modeling errors and the joint friction. Moreover, the estimation error is significant when MOB is applied to high-dimensional floating-base humanoids, which prevents the estimated external joint torque from being used for force control or collision detection in the real humanoid robot. In this paper, the pure external joint torque estimation method named MOB-Net, is proposed for humanoids. MOB-Net learns the model uncertainty torque and calibrates the estimated signal of MOB. The external joint torque can be estimated in the generalized coordinate including whole-body and virtual joints of the floating-base robot with only internal sensors (an IMU on the pelvis and encoders in the joints). Our method substantially reduces the estimation errors of MOB, and the robust performance of MOB-Net for the unseen data is validated through extensive simulations, real robot experiments, and ablation studies. Finally, various collision handling scenarios are presented using the estimated external joint torque from MOB-Net: contact wrench feedback control for locomotion, collision detection, and collision reaction for safety.

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arXiv2024

Efficient Computation of Whole-Body Control Utilizing Simplified Whole-Body Dynamics via Centroidal Dynamics

In this study, we present a novel method for enhancing the computational efficiency of whole-body control for humanoid robots, a challenge accentuated by their high degrees of freedom. The reduced-dimension rigid body dynamics of a floating base robot is constructed by segmenting its kinematic chain into constrained and unconstrained chains, simplifying the dynamics of the unconstrained chain through the centroidal dynamics. The proposed dynamics model is possible to be applied to whole-body control methods, allowing the problem to be divided into two parts for more efficient computation. The efficiency of the framework is demonstrated by comparative experiments in simulations. The calculation results demonstrate a significant reduction in processing time, highlighting an improvement over the times reported in current methodologies. Additionally, the results also shows the computational efficiency increases as the degrees of freedom of robot model increases.

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