arXiv subjects

Ji Ma

Publications and source records attributed to Ji Ma.

At least 19 recordsLinked to original sources

arXiv2026

Unified Motion Retargeting for Humanoids with Learned Point Cloud Correspondence

Humanoid learning increasingly relies on transforming vast and diverse human motion data into high-quality robot reference trajectories. However, retargeting human motion to humanoid robots is challenging due to substantial differences in morphology, degrees of freedom, joint ranges, and kinematic constraints between humans and robots. Existing retargeting methods typically address these differences by defining human-robot correspondence through hand-crafted sparse keypoints or body-part pairs. As a result, retargeting quality depends heavily on manual semantic design, limiting scalability across motion sources and robot morphologies and providing only sparse guidance for reproducing detailed poses and interactions. In this paper, we present Unified Motion Retargeting (UMR), a framework that learns dense point cloud correspondence without requiring manually designed human-robot mappings. By treating exterior point clouds as a unified interface between human motion and humanoid robots, UMR decouples retargeting from source-specific skeletal semantics and robot-specific topology. The learned dense correspondence provides fine-grained geometric anchors for constrained point cloud matching optimization, enabling surface-level pose alignment and direct transfer of interaction contacts. Experiments demonstrate that UMR unifies retargeting across heterogeneous motion sources, robot embodiments, and downstream scenarios ranging from locomotion to interaction, while achieving higher motion fidelity and plausibility than state-of-the-art methods. UMR therefore provides a scalable foundation for transforming large-scale human motion references into robot-ready training data.

cs.RO↗

arXiv2026

Learning Highly Dynamic Skills Transition for Quadruped Jumping Through Constrained Space

Although legged animals are capable of performing explosive motions while traversing confined spaces, replicating this behavior in quadrupedal robots has been a longstanding challenge. Here, we propose a hierarchical reinforcement learning pipeline that empowers the robots to perform aggressive locomotion through constrained obstacles--a narrow gate. The imitation learning technique is used to train the low-level policy, which mimics the behaviors of real animals and forms a set of diverse skills. The high-level controller, having an awareness of the capability of low-level skills and acquiring the gate information via vision-based detection, determines the suitable maneuvers with collision-free trajectories to traverse it dynamically. Notably, we also verify that this framework can be extended to other highly dynamic tasks. This is one of the first works that perform autonomous and agile aerial gate traversal tasks on ground-walking robots, extending the lifelike agility of legged robots to match that of their biological counterparts.

cs.RO↗

arXiv2026

HiPHI: A Large-Scale Benchmark for High-Precision Human Motion and Object-Interaction

Humanoid intelligence requires learning over an extremely diverse space of whole-body motions and physically grounded interactions. However, existing embodied datasets remain fundamentally limited: internet-scale video data lack precise physical states and interaction grounding, while laboratory motion datasets provide high fidelity but only narrow behavioral coverage. This mismatch creates a critical bottleneck for scalable humanoid policy learning. We present HiPHI, a 600+ hour scale high-fidelity whole-body human motion dataset designed to systematically maximize coverage of the human motion and interaction manifold. HiPHI is theoretically guided by FrameNet, a linguistic framework organizing human primitives. Created using an optical motion capture pipeline, HiPHI provides sub-millimeter spatial marker tracking accuracy for full-body human motion and mesh-level object trajectories. We further introduce a benchmark suite evaluating motion-space diversity, interaction grounding, object consistency, and physical AI applications. Our analyses demonstrate that HiPHI significantly expands motion coverage compared to existing motion datasets while maintaining high-fidelity interaction quality, and establishes a scalable data foundation for training, evaluating, and generalizing humanoid policies in real-world embodied tasks, where similar extensions are also applicable to motion prior models in computer graphics. Project page: https://noitom-robotics.github.io/hiphi/

cs.RO↗

arXiv2026

A Master-Slave Robot Manipulator for Needle-Based Teleoperation in MRI Chamber

We present a MR safe, master-slave robot manipulator for abdominal interventions in the MRI chamber. A human operated 2+1-DoF master controller manipulator transmits motion and force to a 2+1-DoF slave manipulator via fluid transmission. Jointly, a digital master controller provides multimodal control capability beyond common split axis or mode switchable hybrid human-digital controller configurations found in previous studies. High input impedance, low-leakage, elastomeric fluid actuators are delegated to remote angulation control. Low-friction graphite piston cylinders are delegated to needle insertion axis remote actuation given the sub-newton force transparency and sub-millimeter motion transmission over bedside fluid piping lengths. The device enables real-time MRI guided interventions allowing manual, digital, hybrid, and collaborative control modes. Collaborative tasks such as assisted tissue penetration, fault-driven virtual fixture, and motion compensation through feedback control are presented in this paper. Preliminary MR scanner results demonstrate manipulator functional viability for an in-vivo pig experiment in bedside, manual control mode configuration.

cs.RO↗

arXiv2026

PFM-HR: Pose Flow Matching for Humanoid Robots

Motion priors improve reinforcement learning for physics-based humanoid tracking, but temporal priors require ordered motion clips, while pose priors provide limited guidance for policy-induced pose transitions. We present Pose Flow Matching for Humanoid Robots (PFM-HR), a reusable flow matching prior trained directly on large scale unordered pose data. PFM-HR introduces the Pose Geometry Score (PGS), which quantifies how joint coordinate changes during rollouts align with the local geometry of pose variation captured by the prior. Using PGS to modulate the tracking reward guides policy exploration toward structured pose changes while keeping the prior frozen across tracking tasks. Experiments demonstrate that PFM-HR improves both single motion and general motion tracking, especially for highly dynamic motions.

cs.RO↗

arXiv2026

OmniContact: Chaining Meta-Skills via Contact Flow for Generalizable Humanoid Loco-Manipulation

Learning long-horizon humanoid loco-manipulation poses a dual challenge: it requires not only the robust execution of meta-skills but also their seamless, closed-loop chaining equipped with autonomous recovery. Existing approaches remain limited: explicit humanoid-object interaction representations offer precision but are notoriously difficult for high-level planning, whereas implicit skill embeddings are compact but lack the interpretability required for reliable composition. We propose \ours, a hierarchical framework centered on \textbf{contact flow (CF)}, a compact representation consisting of key body trajectories and time-series binary contact signals. Leveraging this shared interface, our low-level policy \textbf{CF-Track} learns a unified library of loco-manipulation skills, while our high-level module \textbf{CF-Gen} heuristically synthesizes future contact-flow sequences. To support this setting, we additionally collect the OmniContact dataset, a MoCap-based HOI corpus for humanoid loco-manipulation (Appendix~\ref{sec:dataset}). Together, they enable robust execution, autonomous failure recovery, and flexible composition of meta-skills for long-horizon tasks. Experiments show that OmniContact achieves $98.7\%$ success on \textit{Carry Box} and $76.5\%$ on \textit{Push-Stack Boxes}, outperforming prior baselines by average margins of $40.9\%$ in meta-skill and $66.5\%$ in skill chaining. Besides, our framework naturally integrates with VLMs for semantic task decomposition, enabling complex, semantically grounded loco-manipulation behaviors, such as arranging scattered boxes into a heart shape.

cs.RO↗

arXiv2026

Interfacial Coupling and Sparse Intercalation of 7-Atom-Wide Armchair Graphene Nanoribbons by N-Heterocyclic Carbene Monolayers

Graphene nanoribbons (GNRs) synthesized on metal substrates experience electronic coupling and screening from the underlying surface, which, although often weak, can modify their observed properties and complicate their transfer to device-compatible substrates. Intercalation of GNRs by self-assembled monolayers (SAMs) offers a possible route to reduce this interaction. Here, we investigate the intercalation of 7-atom-wide armchair graphene nanoribbons (7-AGNRs) on Au(111) using N-heterocyclic carbenes (NHCs). Low-temperature scanning tunneling microscopy and spectroscopy, Raman spectroscopy, and density functional theory calculations reveal that the adsorption geometry of the NHCs strongly influences the intercalation yield for GNRs. Methyl-substituted NHCs form flat-lying dimers that partially intercalate the GNRs, producing locally decoupled segments. In contrast, bulkier isopropyl-substituted NHCs form upright monomers that embed the GNRs within the monolayer, preventing intercalation. The low intercalation yield indicates that lifting the nanoribbon from the Au surface is energetically costly. These results establish molecular adsorption geometry and packing as key parameters controlling intercalation at GNR-metal interfaces, with implications for the rational design of decoupling layers for GNR-based device integration pathways.

cond-mat.mtrl-sci↗

arXiv2026

Gemini Embedding 2: A Native Multimodal Embedding Model from Gemini

We introduce Gemini Embedding 2, a native multimodal embedding model that allows embedding video, audio, image, and text modalities in a unified representation space. We leverage the multimodal capabilities of Gemini to produce embeddings for arbitrary combinations of interleaved inputs across all these modalities that generalize well across a wide variety of tasks. Applying large-scale contrastive learning in a multi-task multi-stage training setup, we achieve state-of-the-art performance on key embedding benchmarks including unimodal, cross-modal, and multimodal retrieval spanning a diverse set of tasks. We show that our embedding model demonstrates strong performance (with a score of 62.9 R@1 on MSCOCO, 68.8 NDCG@10 on Vatex, 69.9 on MTEB multilingual and 84.0 on MTEB Code) across a variety of tasks surpassing the performance of specialized models. These unified capabilities make Gemini Embedding 2 a promising candidate for downstream use cases such as RAG, recommendation and search. Furthermore, its robust zero-shot performance across distinct fields - from astronomy and bioscience to fine arts and the culinary arts - establishes it as a highly reliable, out-of-the-box representation even for specialized domains.

cs.CV↗

arXiv2026

Engineering Ferrimagnetic Interactions in Molecular Quantum Systems

Achieving long-range ferrimagnetic order in purely organic systems remains a major challenge in molecular magnetism. Here we report the synthesis and characterization of heterospin-coupling motifs, formed by covalently linking spin-1/2 and spin-1 triangular nanographenes. A combined solution-phase and on-surface synthetic strategy yields three distinct compounds, whose structures are elucidated by bond-resolved scanning probe microscopy. Starting from a spin-1/2--spin-1 dimer as the elemental ferrimagnetic unit, we employ inelastic electron tunneling spectroscopy to resolve low-energy magnetic excitations and extract the parameters of the Heisenberg Hamiltonian. Extension to trimeric architectures results in two distinct spin configurations, with compensated ($S=0$) and uncompensated ($S=3/2$) ferrimagnetic ground states. The Heisenberg model accurately describes all magnetic transitions, offering direct insight into increasingly complex spin Hamiltonians. These findings establish a molecular platform for designing tunable heterospin systems with robust exchange interactions, opening routes toward multi-level spin encoding in qudit-based quantum technologies.

cond-mat.mes-hall↗

arXiv2026

Hallucination-aware intermediate representation edit in large vision-language models

Large Vision-Language Models have demonstrated exceptional performance in multimodal reasoning and complex scene understanding. However, these models still face significant hallucination issues, where outputs contradict visual facts. Recent research on hallucination mitigation has focused on retraining methods and Contrastive Decoding (CD) methods. While both methods perform well, retraining methods require substantial training resources, and CD methods introduce dual inference overhead. These factors hinder their practical applicability. To address the above issue, we propose a framework for dynamically detecting hallucination representations and performing hallucination-eliminating edits on these representations. With minimal additional computational cost, we achieve state-of-the-art performance on existing benchmarks. Extensive experiments demonstrate the effectiveness of our approach, highlighting its efficient and robust hallucination elimination capability and its powerful controllability over hallucinations. Code is available at https://github.com/ASGO-MM/HIRE

cs.CV↗

arXiv2026

Understanding and Mitigating Hallucinations in Multimodal Chain-of-Thought Models

Multimodal Chain-of-Thought (MCoT) models have demonstrated impressive capability in complex visual reasoning tasks. Unfortunately, recent studies reveal that they suffer from severe hallucination problems due to diminished visual attention during the generation process. However, visual attention decay is a well-studied problem in Large Vision-Language Models (LVLMs). Considering the fundamental differences in reasoning processes between MCoT models and traditional LVLMs, we raise a basic question: Whether MCoT models have unique causes of hallucinations? To answer this question, we systematically investigate the hallucination patterns of MCoT models and find that fabricated texts are primarily generated in associative reasoning steps, which we term divergent thinking. Leveraging these insights, we introduce a simple yet effective strategy that can effectively localize divergent thinking steps and intervene in the decoding process to mitigate hallucinations. Extensive experiments show that our method outperforms existing methods by a large margin. More importantly, our proposed method can be conveniently integrated with other hallucination mitigation methods and further boost their performance. The code is publicly available at https://github.com/ASGO-MM/MCoT-hallucination.

cs.CV↗

arXiv2026

Co-jump: Cooperative Jumping with Quadrupedal Robots via Multi-Agent Reinforcement Learning

While single-agent legged locomotion has witnessed remarkable progress, individual robots remain fundamentally constrained by physical actuation limits. To transcend these boundaries, we introduce Co-jump, a cooperative task where two quadrupedal robots synchronize to execute jumps far beyond their solo capabilities. We tackle the high-impulse contact dynamics of this task under a decentralized setting, achieving synchronization without explicit communication or pre-specified motion primitives. Our framework leverages Multi-Agent Proximal Policy Optimization (MAPPO) enhanced by a progressive curriculum strategy, which effectively overcomes the sparse-reward exploration challenges inherent in mechanically coupled systems. We demonstrate robust performance in simulation and successful transfer to physical hardware, executing multi-directional jumps onto platforms up to 1.5 m in height. Specifically, one of the robots achieves a foot-end elevation of 1.1 m, which represents a 144% improvement over the 0.45 m jump height of a standalone quadrupedal robot, demonstrating superior vertical performance. Notably, this precise coordination is achieved solely through proprioceptive feedback, establishing a foundation for communication-free collaborative locomotion in constrained environments.

cs.RO↗

arXiv2026

Learning Human-Like Badminton Skills for Humanoid Robots

Realizing versatile and human-like performance in high-demand sports like badminton remains a formidable challenge for humanoid robotics. Unlike standard locomotion or static manipulation, this task demands a seamless integration of explosive whole-body coordination and precise, timing-critical interception. While recent advances have achieved lifelike motion mimicry, bridging the gap between kinematic imitation and functional, physics-aware striking without compromising stylistic naturalness is non-trivial. To address this, we propose Imitation-to-Interaction, a progressive reinforcement learning framework designed to evolve a robot from a "mimic" to a capable "striker." Our approach establishes a robust motor prior from human data, distills it into a compact, model-based state representation, and stabilizes dynamics via adversarial priors. Crucially, to overcome the sparsity of expert demonstrations, we introduce a manifold expansion strategy that generalizes discrete strike points into a dense interaction volume. We validate our framework through the mastery of diverse skills, including lifts and drop shots, in simulation. Furthermore, we demonstrate the first zero-shot sim-to-real transfer of anthropomorphic badminton skills to a humanoid robot, successfully replicating the kinetic elegance and functional precision of human athletes in the physical world.

cs.RO↗

arXiv2026

Massive Sound Embedding Benchmark (MSEB)

Audio is a critical component of multimodal perception, and any truly intelligent system must demonstrate a wide range of auditory capabilities. These capabilities include transcription, classification, retrieval, reasoning, segmentation, clustering, reranking, and reconstruction. Fundamentally, each task involves transforming a raw audio signal into a meaningful 'embedding' - be it a single vector, a sequence of continuous or discrete representations, or another structured form - which then serves as the basis for generating the task's final response. To accelerate progress towards robust machine auditory intelligence, we present the Massive Sound Embedding Benchmark (MSEB): an extensible framework designed to evaluate the auditory components of any multimodal system. In its first release, MSEB offers a comprehensive suite of eight core tasks, with more planned for the future, supported by diverse datasets, including the new, large-scale Simple Voice Questions (SVQ) dataset. Our initial experiments establish clear performance headrooms, highlighting the significant opportunity to improve real-world multimodal experiences where audio is a core signal. We encourage the research community to use MSEB to assess their algorithms and contribute to its growth. The library is publicly hosted at github.

cs.SD↗

arXiv2025

Retrieval Augmented Question Answering: When Should LLMs Admit Ignorance?

The success of expanded context windows in Large Language Models (LLMs) has driven increased use of broader context in retrieval-augmented generation. We investigate the use of LLMs for retrieval augmented question answering. While longer contexts make it easier to incorporate targeted knowledge, they introduce more irrelevant information that hinders the model's generation process and degrades its performance. To address the issue, we design an adaptive prompting strategy which involves splitting the retrieved information into smaller chunks and sequentially prompting a LLM to answer the question using each chunk. Adjusting the chunk size allows a trade-off between incorporating relevant information and reducing irrelevant information. Experimental results on three open-domain question answering datasets demonstrate that the adaptive strategy matches the performance of standard prompting while using fewer tokens. Our analysis reveals that when encountering insufficient information, the LLM often generates incorrect answers instead of declining to respond, which constitutes a major source of error. This finding highlights the need for further research into enhancing LLMs' ability to effectively decline requests when faced with inadequate information.

cs.CL↗

arXiv2025

Electrical Modulation and Probing of Antiferromagnetism in Hybrid Multiferroic Heterostructures

The unique features of ultrafast spin dynamics and the absence of macroscopic magnetization in antiferromagnetic (AFM) materials provide a distinct route towards high-speed magnetic storage devices with low energy consumption and high integration density. However, these advantages also introduce challenges in probing and controlling AFM order, thereby restricting their practical applications. In this study, we demonstrate an all-electric control and probing of the AFM order in heavy metal (HM)/AFM insulator (AFMI) heterostructures on a ferroelectric substrate at room temperature (RT). The AFM order was detected by the anomalous Hall effect (AHE) and manipulated by the ferroelectric field effect as well as the piezoelectric effect in heterostructures of Pt/NiO/0.7Pb(Mg$_{1/3}$Nb$_{2/3}$)O$_{3}$--0.3PbTiO$_{3}$ (PMN--PT). The non-volatile control of AFM order gives rise to a 33\% modulation of AHE, which is further evidenced by synchrotron-based X-ray magnetic linear dichroism (XMLD). Combined with the $in$-$situ$ piezoelectric response of AHE, we demonstrate that ferroelectric polarization contributes mainly to the control of the AFM order. Our results are expected to have broader implications for efficient spintronic devices.

cond-mat.mtrl-sci↗

arXiv2025

High-Precision Surgical Robotic System for Intraocular Procedures

Despite the extensive demonstration of robotic systems for both cataract and vitreoretinal procedures, existing technologies or mechanisms still possess insufficient accuracy, precision, and degrees of freedom for instrument manipulation or potentially automated tool exchange during surgical procedures. A new robotic system that focuses on improving tooltip accuracy, tracking performance, and smooth instrument exchange mechanism is therefore designed and manufactured. Its tooltip accuracy, precision, and mechanical capability of maintaining small incision through remote center of motion were externally evaluated using an optical coherence tomography (OCT) system. Through robot calibration and precise coordinate registration, the accuracy of tooltip positioning was measured to be 0.053$\pm$0.031 mm, and the overall performance was demonstrated on an OCT-guided automated cataract lens extraction procedure with deep learning-based pre-operative anatomical modeling and real-time supervision.

cs.RO↗

arXiv2025

Integrating Transparent Models, LLMs, and Practitioner-in-the-Loop: A Case of Nonprofit Program Evaluation

Public and nonprofit organizations often hesitate to adopt AI tools because most models are opaque even though standard approaches typically analyze aggregate patterns rather than offering actionable, case-level guidance. This study tests a practitioner-in-the-loop workflow that pairs transparent decision-tree models with large language models (LLMs) to improve predictive accuracy, interpretability, and the generation of practical insights. Using data from an ongoing college-success program, we build interpretable decision trees to surface key predictors. We then provide each tree's structure to an LLM, enabling it to reproduce case-level predictions grounded in the transparent models. Practitioners participate throughout feature engineering, model design, explanation review, and usability assessment, ensuring that field expertise informs the analysis at every stage. Results show that integrating transparent models, LLMs, and practitioner input yields accurate, trustworthy, and actionable case-level evaluations, offering a viable pathway for responsible AI adoption in the public and nonprofit sectors.

cs.CY↗