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Hanbit Oh

Publications and source records attributed to Hanbit Oh.

At least 19 recordsLinked to original sources
arXiv2026

Thermal Phase Transition of Three Dimensional Orthogonal Metals

Orthogonal metals provide a paradigmatic example of strongly entangled metallic states whose transport and thermodynamic properties resemble those of conventional Fermi liquids, while their single-electron excitations remain fundamentally distinct. Despite considerable interest in their unusual ground-state properties, orthogonal metals in three spatial dimensions and their finite-temperature behavior remain relatively unexplored. Here, we investigate a three-dimensional orthogonal metal and characterize the thermodynamic and spectroscopic signatures associated with its finite-temperature phase transition. Starting from a simple model of fermions coupled to spin degrees of freedom, we perturbatively demonstrate that the thermal transition known to occur in the purely bosonic limit persists in the presence of coupling to a gapless Fermi surface. We then examine physical observables that change prominently between the coherent and orthogonal-metal regimes. In particular, we calculate the evolution of the physical-electron spectral function and identify characteristic signatures in angle-resolved photoemission spectroscopy. We find a pronounced contrast in low-energy spectral weight between the coherent and orthogonal-metal regimes without requiring conventional symmetry-breaking order. Our results establish finite-temperature orthogonal-metal physics as a possible mechanism for pseudogap-like behavior in three-dimensional and quasi-two-dimensional layered materials with non-negligible interlayer hopping, which may be applied to the recent experiments in $\mathrm{Sr_2 VO_3 Fe As}$.

cond-mat.str-el↗
arXiv2026

Evaluating Tactile Foundation Models for End-to-End Imitation Learning in Contact-Rich Manipulation

Tactile sensing plays an important role in imitation learning for contact-rich manipulation by providing direct observations of physical contact with objects. Recently, several tactile foundation models (TFMs) have been proposed to learn general-purpose tactile representations through pretraining on large-scale tactile datasets. However, how these pretrained tactile representations should be leveraged for end-to-end imitation learning remains insufficiently explored. In this work, we integrate multiple TFMs into Diffusion Policy and systematically evaluate their effectiveness for contact-rich manipulation under common experimental settings. We further investigate adaptation strategies, including encoder fine-tuning and task-specific mid-training, to understand how pretrained tactile representations can be effectively used for downstream imitation learning. Through extensive evaluation across diverse manipulation tasks in both real-world and simulation environments, including more than 5,000 real-world policy rollouts, we provide empirical insights into the use of TFMs for end-to-end imitation learning. Project page: https://mmurooka.github.io/tfm4il-project-page/

cs.RO↗
arXiv2026

TOAST: Stochastic Robot Action Tokenization for Autoregressive Vision-Language-Action Models

Autoregressive Vision-Language-Action models often represent continuous robot actions as discrete token sequences, enabling action prediction with standard next-token objectives. FAST has substantially improved this representation by compactly encoding action containing diverse temporal frequencies into relatively few tokens. However, while such compression reduces the number of action tokens required for autoregressive prediction, it does not necessarily improve the efficiency of policy learning from limited demonstrations. In particular, FAST typically assigns a single deterministic tokenization to each quantized action sequence, although multiple token sequences can represent and decode to the same robot motion. We investigate whether exploiting this representational redundancy can improve policy learning. In this paper, we propose TOkenization of Action sequences with STochastic sampling (TOAST), a stochastic action tokenization method that samples alternative tokenizations of the same quantized action sequence during policy training. This diversifies the discrete supervision while preserving the underlying robot action and requires no additional demonstrations. Experiments on LIBERO show that TOAST consistently improves over its deterministic counterpart, with the improvement increasing as training data decreases, achieving a 6.8 point gain in success rate when only 1/16 of training data is available. Across four real-robot manipulation tasks, TOAST further improves mean success rate by 15.8 points over the deterministic counterpart. These results demonstrate the effectiveness of stochastic action tokenization for autoregressive robot policy learning, particularly when training data are limited.

cs.RO↗
arXiv2026

EgoSpeedUp: Transferring Human Manipulation Tempo to Robot Policies

Robot manipulation policies trained through imitation learning inherit not only the demonstrated behavior but also the conservative execution tempo of robot demonstrations. Existing acceleration approaches can execute faster than the original demonstrations, but determine the appropriate acceleration primarily from robot-side information or a predefined set of tempo factors, leaving open how to obtain a task-appropriate reference for how fast each manipulation phase should progress. We introduce EgoSpeedUp, a framework that uses human manipulation as temporal supervision for robot imitation learning. Our key insight is that human demonstrations naturally reveal task-appropriate, phase-wise manipulation tempo. Given slow robot demonstrations and human demonstrations of the same task, EgoSpeedUp aligns corresponding manipulation phases, estimates their relative execution tempos from multiple human demonstrations, and transfers the resulting phase-wise tempo by retiming the robot demonstrations. The retimed demonstrations are then used for standard behavior cloning, allowing the robot to retain its executable manipulation behavior while learning to perform it at a human-informed tempo. Across two real-world manipulation tasks, EgoSpeedUp improves the task success rate by an average of 25 percentage points (pp) while reducing successful execution time by 36.5%. These results demonstrate that human manipulation tempo provides an effective temporal reference for learning faster and more reliable robot policies.

cs.RO↗
arXiv2026

Self-Supervised Anchoring of Fingertip Sensing to Proprioception and Proactive Actions for Robot Imitation Learning

Robotic imitation learning often relies on external cameras, yet local interaction cues such as object proximity, contact onset, and grasp state are difficult to observe near the fingertips because of occlusion and limited temporal resolution. We study how to effectively incorporate complementary fingertip sensing into imitation learning using pressure-sensitive tactile and reflective proximity sensors, along with pretrained sensor encoders. The two modalities provide information at different manipulation phases: proximity sensing is informative before contact, whereas tactile sensing becomes informative after contact. However, naively adding these signals to a policy does not consistently improve performance and can even underperform vision-only policies, suggesting that sparse, phase-dependent sensor signals are difficult to exploit from limited demonstrations. We therefore propose a proprioception-anchored pretraining method, PROprioceptive-and-PRoactive Anchoring (PROPRA), which independently aligns each fingertip sensor history with proprioceptive and action segments. This provides a continuously available sensorimotor reference, allowing each sensor to be aligned independently during its informative phases. Experiments on real-world manipulation tasks show that our pretraining method improves average success rates over vision-only policies and image-anchored pretraining baselines. Representation analysis further shows that it preserves richer information about pre-contact states, enabling more effective use of complementary fingertip sensing. Please refer to our project page: https://tomohiromotoda.github.io/nia.propra/

cs.RO↗
arXiv2026

The Embodiment Gap in Robot Foundation Models

Robot foundation models (RFMs), including vision-language-action (VLA) policies, are often discussed through a scaling view: more data, larger models, and broader benchmarks should improve generalization. In robotics, however, a model can generalize while work still remains before it can run on a robot with a particular body. The work required differs across methods and target robots, and those differences affect practical deployment. We call the gap between reusable models, representations, or data and their use in execution on the target robot the embodiment gap. This survey examines what can be reused across robot embodiments and what must still be implemented on a new robot. We place existing methods on a two-axis map that shows the type of shared structure and the stage at which adaptation is needed for execution on the target robot. We then examine recent work through three overlapping research directions: sharing semantics and perception, sharing robot data and interfaces, and learning correspondence across embodiments. We also propose a reporting framework for adaptation work that success rate alone does not reveal. The framework identifies the work that should be checked when comparing cross-embodiment learning and highlights work that remains on a new robot and questions for future study.

cs.RO↗
arXiv2026

GuidedAttention: Interpretable and Correctable Visual Attention for OOD-Robust Robot Manipulation via Imitation Learning

End-to-end visuomotor policies provide little opportunity for humans to understand or correct the policy's visual attention. We propose GuidedAttention, a visuomotor imitation learning framework that introduces interpretable and correctable visual attention as an explicit intermediate representation. Task-relevant attention keypoints are predicted from camera images and condition a diffusion-based action policy. Users can inspect and optionally correct selected keypoints once at rollout initialization, after which the corrected attention is automatically propagated throughout execution by a tracking module. Experiments in simulation and the real world demonstrate that GuidedAttention consistently improves robot manipulation performance, particularly under positional and appearance out-of-distribution (OOD) conditions. https://mmurooka.github.io/guided-attention-project-page

cs.RO↗
arXiv2026

RoboManipBaselines: A Unified Framework for Imitation Learning in Robotic Manipulation across Real and Simulation Environments

We present RoboManipBaselines, an open-source software framework for imitation learning research in robotic manipulation. The framework supports the entire imitation learning pipeline, including data collection, policy training, and rollout, across both simulation and real-world environments. Its design emphasizes integration through a consistent workflow, generality across diverse environments and robot platforms, extensibility for easily adding new robots, tasks, and policies, and reproducibility through evaluations using publicly available datasets. RoboManipBaselines systematically implements the core components of imitation learning: environment, dataset, and policy. Through a unified interface, the framework supports multiple simulators and real robot environments, as well as multimodal sensors and a wide variety of policy models. We further present benchmark evaluations in both simulation and real-world environments and introduce several research applications, including data augmentation, integration with tactile models, interactive robotic systems, 3D sensing evaluation, and hardware extensions. These results demonstrate that RoboManipBaselines provides a useful foundation for advancing research and experimental validation in robotic manipulation using imitation learning. https://isri-aist.github.io/RoboManipBaselines-ProjectPage

cs.RO↗
arXiv2026

Origin of Spin Stripes in Bilayer Nickelate La$_3$Ni$_2$O$_7$

The bilayer nickelate La$_3$Ni$_2$O$_7$ has recently emerged as a high-temperature superconductor with unusual spin stripe order in its ambient pressure phase. We propose a microscopic Hamiltonian that faithfully reflects the crystalline symmetry of this system, with the primary aim of addressing its unconventional magnetism at ambient pressure. Using state-of-the-art density matrix renormalization group calculations, we show that $(π/2,π/2)$ spin stripe order arises in our model at sizable Hund's coupling $J_H$ from a hidden quasi-one-dimensionality and persists over a range of electron concentrations. In the more symmetric high-pressure regime, our model exhibits enhanced interlayer pairing tendencies when the interlayer antiferromagnetic coupling $J_{\bot}$ becomes sufficiently large. Our results provide a microscopic origin of the diagonal spin stripes and identify Hund's coupling $J_H$ and interlayer coupling $J_{\bot}$ as key ingredients governing magnetic order and pairing tendencies in La$_3$Ni$_2$O$_7$.

cond-mat.supr-con↗
arXiv2026

Self-Augmented Robot Trajectory: Efficient Imitation Learning via Safe Self-augmentation with Demonstrator-annotated Precision

Imitation learning is a promising paradigm for training robot agents; however, standard approaches typically require substantial data acquisition -- via numerous demonstrations or random exploration -- to ensure reliable performance. Although exploration reduces human effort, it lacks safety guarantees and often results in frequent collisions -- particularly in clearance-limited tasks (e.g., peg-in-hole) -- thereby, necessitating manual environmental resets and imposing additional human burden. This study proposes Self-Augmented Robot Trajectory (SART), a framework that enables policy learning from a single human demonstration, while safely expanding the dataset through autonomous augmentation. SART consists of two stages: (1) human teaching only once, where a single demonstration is provided and precision boundaries -- represented as spheres around key waypoints -- are annotated, followed by one environment reset; (2) robot self-augmentation, where the robot generates diverse, collision-free trajectories within these boundaries and reconnects to the original demonstration. This design improves the data collection efficiency by minimizing human effort while ensuring safety. Extensive evaluations in simulation and real-world manipulation tasks show that SART achieves substantially higher success rates than policies trained solely on human-collected demonstrations. Video results available at https://sites.google.com/view/sart-il .

cs.RO↗
arXiv2026

Incommensurate pair-density-wave correlations in two-leg ladder $t$--$J$--$J_\perp$ model

We report the discovery of a generalized Luther-Emery liquid phase characterized by incommensurate pair-density-wave (iC-PDW) correlations in the two-leg $t$-$J$-$J_\perp$ ladder model. By tuning the potential difference between the legs, we explore the regime of intermediate layer polarization $P$. Combining density-matrix renormalization group (DMRG) simulations with bosonization analysis, we identify a spin-gapped phase at finite $P$, where the interlayer and intralayer pair correlations both oscillate, but with distinct periodicities. The interlayer correlations exhibit FFLO-like oscillations, driven by pairing between layers with mismatched Fermi momenta, with a period determined by their momentum difference. In contrast, the intralayer pair correlations arise from the coupling between charges on one layer and spin fluctuations on the opposite layer, with a momentum equal to twice the Fermi momentum of the opposite layer. The iC-PDW state is robust across a wide range of doping and polarization, although finite interlayer hopping eventually destabilizes it toward a state with charge-$4e$ correlations. We conclude by discussing the experimental realization of this model in optical lattice platforms and its relevance to the bilayer nickelate La$_3$Ni$_2$O$_7$.

cond-mat.str-el↗
arXiv2026

High spin, low spin or gapped spins: magnetism in the bilayer nickelates

Inspired by the recent discovery of high-temperature superconductivity in bilayer nickelates, we investigate the role of magnetism emerging from a hypothetical insulating $d^8$ parent state. We demonstrate that due to the interplay of superexchange and Hund's coupling, the system can be in a high-spin, low-spin or spin-gapped state. The low-spin state has singlets across the bilayer in the $d_{z^2}$ orbital, with charge carriers in the $d_{x^2-y^2}$ orbital. Thus, at low energy scales, it behaves as an effective one band system when hole doped. By contrast, the high-spin state is a more robust, spin-1 antiferromagnet. Using Hartree-Fock methods, we find that for fixed interaction strength and doping, high-spin magnetism remains more robust than the low-spin counterpart. Whether this implies that the high spin state provides a stronger pairing glue, or more strongly competes with superconductivity remains an open question. Our analysis therefore underscores the importance of identifying the spin state for understanding superconductivity in nickelates.

cond-mat.str-el↗
arXiv2025

Pair-density-wave superconductivity and Anderson's theorem in bilayer nickelates

The recent experimental observations of high temperature superconductivity in bilayer nickelate have attracted lots of attentions. Previous studies have assumed a mirror symmetry $\mathcal M$ between the two layers and focused on uniform and clean superconducting states. Here, we show that breaking this mirror symmetry via an applied displacement field can stabilize a pair-density-wave (PDW) superconductor, which is similar to the Fulde--Ferrell--Larkin--Ovchinnikov (FFLO) state, but at zero magnetic field. Based on a mean-field analysis of a model of $d_{x^2-y^2}$ orbital with an effective inter-layer attraction, we demonstrate that the PDW phase is robust over a wide range of displacement field, interlayer hopping strengths, and electron fillings. Finally, we analyze disorder effects on interlayer superconductivity within the first Born approximation. Based on symmetry considerations, we show that pairing is weaken by disorders which break the mirror symmetry, even with unbroken time reversal symmetry. Our results establish bilayer nickelate as a tunable platform for realizing finite-momentum pairing and for exploring generalized disorder effects.

cond-mat.str-el↗
arXiv2025

Doping a spin-one Mott insulator: possible application to bilayer nickelate

In this article, we review some recent theoretical developments on potential high-temperature superconductors and unconventional metallic states that can arise from doping a spin-one Mott insulator in the $d^{8}$ valence. These studies are particularly relevant-though not limited-to the recently discovered bilayer nickelate superconductor La$_3$Ni$_2$O$_7$. We focus on a ferromagnetic (FM) Kondo lattice model with mobile electrons in the $d_{x^2-y^2}$ orbital coupled to the localized spin moments in $d_{z^2}$ orbital through a large Hund's coupling $J_H$. In the large $J_H$ limit, the model reduces to the type II t-J model with a mixture of spin-half singlon states and spin-one doublon states. We summarize DMRG results on the Luther-Emery liquid in one dimensional chain and two-leg ladder. Then we mainly focus on bilayer square lattice and show that a large inter-layer coupling $J_\perp$ of $d_{z^2}$ orbital can induce strong inter-layer pairing of $d_{x^2-y^2}$ orbital. In the strong $J_\perp$ limit, a kinetic-energy driven high $T_c$ superconductivity is demonstrated in an ideal model with only a single hopping term. Furthermore, the model predicts a symmetric pseudogap metal-dubbed `second Fermi liquid"-in the underdoped regime, yielding a phase diagram analogous to that of hole-doped cuprates. The bilayer Kondo model therefore, presents a promising platform for both realizing higher-Tc superconductors and exploring non-Fermi liquid physics. We also comment on the possible limitations of the current models for the bilayer nickelate material and point out some future directions.

cond-mat.str-el↗
arXiv2025

Robust Instant Policy: Leveraging Student's t-Regression Model for Robust In-context Imitation Learning of Robot Manipulation

Imitation learning (IL) aims to enable robots to perform tasks autonomously by observing a few human demonstrations. Recently, a variant of IL, called In-Context IL, utilized off-the-shelf large language models (LLMs) as instant policies that understand the context from a few given demonstrations to perform a new task, rather than explicitly updating network models with large-scale demonstrations. However, its reliability in the robotics domain is undermined by hallucination issues such as LLM-based instant policy, which occasionally generates poor trajectories that deviate from the given demonstrations. To alleviate this problem, we propose a new robust in-context imitation learning algorithm called the robust instant policy (RIP), which utilizes a Student's t-regression model to be robust against the hallucinated trajectories of instant policies to allow reliable trajectory generation. Specifically, RIP generates several candidate robot trajectories to complete a given task from an LLM and aggregates them using the Student's t-distribution, which is beneficial for ignoring outliers (i.e., hallucinations); thereby, a robust trajectory against hallucinations is generated. Our experiments, conducted in both simulated and real-world environments, show that RIP significantly outperforms state-of-the-art IL methods, with at least $26\%$ improvement in task success rates, particularly in low-data scenarios for everyday tasks. Video results available at https://sites.google.com/view/robustinstantpolicy.

cs.RO↗
arXiv2025

High-temperature superconductivity from kinetic energy

Superconductivity is usually assumed to arise from attractive interaction. In this work we show that strong pairing is possible soley from kinetic energy even without a net attraction. We demonstrate a high-temperature kinetic superconductor in a simple lattice model with nearest-neighbor hopping ($t$) projected onto a constrained Hilbert space, analogous to the $t$-$J$ model with $J=0$, where kinetic magnetism has been previously studied. Using density matrix renormalization group (DMRG) on cylinders up to width $L_y=8$, we find a superconducting ground state exhibiting a key difference from high-$T_c$ cuprates: both the pairing gap and phase stiffness \textit{increase} with doping ($x$). We find pairing gaps, determined from spin and single-electron charge gaps, exceeding $1.5t$. This model can be realized within the double Kondo lattice model, relevant to bilayer nickelates, in the limit of strong inter-layer spin coupling ($J_\perp/t \rightarrow +\infty$) and a balancing inter-layer repulsion ($V$). Importantly, the double Kondo model does not fundamentally restrict $J_\perp/t$, suggesting the potential for high critical temperatures ($T_c$) approaching $0.5t$. While this idealized limit predicts large pairing gaps, we show a smooth connection to the more realistic regime with $J_\perp \sim t$, albeit with a reduced pairing gap of approximately $0.1t$. Assuming $t \sim 10^3$ K in typical solid state systems, our model suggests the exciting possibility of achieving $T_c$ of hundreds of Kelvin. We propose searching for bilayer materials with reduced out-of-plane lattice constants to better approximate the conditions of our ideal model.

cond-mat.str-el↗
arXiv2024

Strong pairing and symmetric pseudogap metal in double Kondo lattice model: from nickelate superconductor to tetralayer optical lattice

In this work, we propose and study a double Kondo lattice model which hosts robust superconductivity. The system consists of two identical Kondo lattice model, each with Kondo coupling $J_K$ within each layer, while the localized spin moments are coupled together via an inter-layer on-site antiferromagnetic spin coupling $J_\perp$. We consider the strong $J_\perp$ limit, wherein the local moments tend to form rung singlets and are thus gapped. However, the Kondo coupling $J_K$ transmits the inter-layer entanglement between the local moments to the itinerant electrons. Consequently, the itinerant electrons experience a strong inter-layer antiferromangetic spin coupling and form strong inter-layer pairing, which is confirmed through numerical simulation in one dimensional system. Experimentally, the $J_K \rightarrow -\infty$ limits of the model describes the recently found bilayer nickelate La$_3$Ni$_2$O$_7$, while the $J_K>0$ side can be realized in tetralayer optical lattice of cold atoms. Two extreme limits, $J_K \rightarrow -\infty$ and $J_K \rightarrow +\infty$ limit are shown to be simplified to a bilayer type II t-J model and a bilayer one-orbital t-J model, respectively. Thus, our double Kondo lattice model offers a unified framework for nickelate superconductor and tetralayer optical lattice quantum simulator upon changing the sign of $J_K$. We highlight both the qualitative similarity and the quantitative difference in the two sides of $J_K$. Finally, we discuss the possibility of a symmetric Kondo breakdown transition in the model with a symmetric pseudogap metal corresponding to the usual heavy Fermi liquid.

cond-mat.str-el↗
arXiv2024

Type II t-J model in charge transfer regime in bilayer La$_3$Ni$_2$O$_7$ and trilayer La$_4$Ni$_3$O$_{10}$

Recent observations of an 80 K superconductor in La$_3$Ni$_2$O$_7$ under high pressure have attracted significant attention. Recent experiments indicate that La$_3$Ni$_2$O$_7$ may be in the charge transfer regime, challenging the previous models based purely on the Ni $d_{x^2-y^2}$ and $d_{z^2}$ orbitals. In this study, we propose a low energy model that incorporates doped holes in the oxygen $p$ orbitals. Given that the parent nickel state is in the $3d^{8}$ configuration with a spin-one moment, doped hole only screens it down to spin-half, in contrast to the Zhang-Rice singlet in cuprate. We dub the single hole state as Zhang-Rice spin-half and build an effective model which includes three spin-one states ($d^8$) and two Zhang-Rice spin-half states ($d^8 L$). At moderate pressure around $20$ GPa, the dominated oxygen orbital is an in-plane Wannier orbital with the same lattice symmetry as the $d_{x^2-y^2}$ orbital. The resulting model reduces to the bilayer type II t-J model previously proposed in the Mott-Hubbard regime. Notably, the hopping between the in-plane $p$ orbitals of the two layers is still suppressed. Density matrix renormalization group (DMRG) simulation reveals a pairing dome with the optimal hole doping level at $x=0.4\sim0.5$, distinct from the hole doped cuprate where optimal doping occurs around $x=0.19$. Further increasing pressure initially raises the critical temperature ($T_c$) until reaching an optimal pressure beyond which the $p_z$ orbital of oxygen becomes favorable and superconductivity is diminished. This shift from in-plane $p$ orbital to $p_z$ orbital may elucidate the experimentally observed superconducting dome with varying pressure. As an extension, we also suggest a trilayer version of the type II t-J model as the minimal model for pressured La$_4$Ni$_3$O$_{10}$, which is distinct from the models in the Mott-Hubbard regime.

cond-mat.str-el↗