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Jiaming Wang

Publications and source records attributed to Jiaming Wang.

At least 19 recordsLinked to original sources
arXiv2026

WorldSketch: Sketching World Models for Zero-Shot Mobile Manipulation

Most agentic robotic systems use vision-language models (VLMs) either to orchestrate predefined skills or to directly generate actions, constraints, and control programs. The former is limited by the available skill library, while the latter requires VLMs to reason about precise geometry and contact. In this paper, we propose WorldSketch, a new paradigm for agentic robotics in which a VLM constructs a world model rather than generating robot actions. The VLM acts as an agent that actively queries perception tools to build and visually validate a minimal kinematic world model, capturing only task-relevant objects, their interaction geometry, and possible motions. A geometric planner then jointly optimizes contact and whole-body motion on the resulting model. Moreover, the world model is self-improving: execution feedback verifies outcomes, updates uncertain hypotheses, and enables the agent to revise the model when its predictions are contradicted. We demonstrate the generality of this paradigm across RoboCasa, LIBERO-PRO, and R2R-CE navigation, achieving zero-shot success rates of 91%, 97%, and 76%, respectively. On RoboCasa, WorldSketch outperforms the next-best zero-shot agent baseline by 58 percentage points while using 10 times fewer VLM input tokens; on LIBERO-PRO, it exceeds CaP-X by 82 percentage points. In navigation, it matches the success rate of step-by-step VLM agents while using 6 times fewer input tokens.

cs.RO↗
arXiv2026

Single-Mode Squeezing Using Degenerate Four-Wave Mixing in a Dual-Pump TWPA

Single-mode squeezing of the electromagnetic vacuum is a key resource for mode-selective noise reduction in quantum measurements. At microwave frequencies, single-mode squeezing is typically obtained by using Josephson and Traveling-Wave Parametric Amplifiers designed for three-wave mixing operation. In single-pump degenerate four-wave mixing, the coincidence of pump, signal, and idler frequencies complicates separation of the squeezed field from the strong pump. A dual-pump configuration enables degenerate operation while keeping both pumps spectrally separated from the squeezed mode. Here, we demonstrate single-mode squeezing in a flux-tunable, dual-pump Traveling-Wave Parametric Amplifier, employing reversed-Kerr phase matching. By independently calibrating the single-pump gains, we find that the strongest squeezing occurs along approximately balanced-gain trajectories, while the optimal squeezing does not necessarily correspond to the highest dual-pump gain. At flux biases $Φ_\mathrm{ext}/Φ_0 = 0.0$ and $0.5$, the minimum quadrature noise levels reach $-2.48 \pm 0.18~\mathrm{dB}$ and $-1.62 \pm 0.20~\mathrm{dB}$ relative to vacuum, respectively. These results establish the proposed architecture as a platform for single-mode squeezing in microwave continuous-variable quantum technologies.

quant-ph↗
arXiv2026

SketchMate: Agentic Geometric Reasoning for Zero-Shot Precision Insertion

Inserting an unseen part requires sub-millimetre precision and the correct orientation of keyed features. Current zero-shot agentic manipulation methods built on vision-language models (VLMs) produce spatial outputs that are far too coarse for this. We propose to let a VLM build a sketch model of the task, i.e., a few geometric primitives and two mate frames, which a VLM agent measures with geometric tools in multi-view depth and which a standard planner and compliant controller execute. Because a sketch describes the parts independently of their placement, the robot needs to build it only once per task. A sketch memory stores each agent run and transfers the stored sketch to later placements by point-cloud registration, so that the VLM is called only for unfamiliar parts. After grasping the part, the robot inspects both mating faces at close range and corrects the sketch by shape matching. On 69 simulated insertion episodes, our method inserts 88% of the parts at the assembled pose in a strict kinematic test, while ReKep and MOKA with the same VLM insert none. On new placements of eight tasks, reusing one stored run inserts as many parts as calling the agent in each episode, and it produces the sketch in 3.4 s instead of about 90 s.

cs.RO↗
arXiv2026

Scale-Dependent Statistics of Coherent Magnetic Structures in Early IMAP Observations

We analyze early Interstellar Mapping and Acceleration Probe (IMAP) magnetic field observations (November 2025 to May 2026) to quantify the statistical properties of helical magnetic structures and their relationship with turbulence intermittency. Intermittent structures, such as current sheets, are identified using the Partial Variance of Increments (PVI) method, and the scale-dependent normalized magnetic helicity is computed over scales corresponding to 0.5-3 correlation lengths. Prior studies have typically applied the PVI-helicity method to individual events; here we extend this technique to the ensemble statistics of a multi-month sample spanning many correlation times. To separate coherent helical structures from random turbulent fluctuations, significance thresholds for helicity are determined using phase-randomized surrogate realizations. We find a clear scale dependence in the occurrence, duration, and waiting time of coherent magnetic helicity, reminiscent of the scale-transfer arguments underlying inverse cascade, selective decay, and Taylor relaxation theories. Event duration and waiting-time distributions are well described by power-law and exponential models over different ranges, consistent with previous findings. In addition, PVI and magnetic helicity are anticorrelated, suggesting that high-PVI regions are preferentially associated with the boundaries of flux ropes, while their interiors tend to exhibit lower PVI and stronger magnetic helicity.

physics.space-ph↗
arXiv2026

Beyond Policy Discrepancy: Capability-Aware Multi-Teacher On-Policy Distillation

Multi-teacher on-policy distillation (MOPD) integrates domain specialists into a shared student, but determining how strongly each teacher should supervise remains challenging. Existing discrepancy-based allocation can assign strong supervision even when the student already matches or exceeds the teacher's task performance, since behavioral mismatch does not necessarily imply a remaining capability deficit. We propose Capability-Aware Multi-Teacher On-Policy Distillation (CA-MOPD), which combines specialist merging with capability-guided allocation to account for what the student has already learned. CA-MOPD first merges reinforcement-learning specialists into a strong student initialization, then uses verifier-estimated teacher--student capability gaps to adjust domain-level distillation strength. Supervision decreases as capability gaps close and increases when estimated student capability regresses, while dense token-level guidance remains provided by the teachers. Across six benchmarks spanning mathematics, code generation, and instruction following, CA-MOPD achieves an average score of 32.22, outperforming naive MOPD, Open-MOPD, and the merged initialization by 0.86, 0.83, and 0.74 points, respectively. Controlled comparisons further support the benefits of merged initialization and capability-gap guidance under matched training budgets.

cs.LG↗
arXiv2026

Control-Ready Uncertainty for Trajectory Diffusion

Diffusion models can represent complex, multimodal trajectory distributions, but extracting uncertainty from them typically requires costly Monte Carlo sampling. This limits their use in real-time control, where robots must rapidly assess risk and maintain safety margins. We introduce Score-Curvature for Online Precision Estimation (SCOPE), a lightweight module that augments diffusion trajectory models with control-ready uncertainty. SCOPE learns a structured precision matrix around each nominal trajectory by distilling score-curvature information and producing calibrated Gaussian tubes with low overhead and without repeated Monte Carlo sampling. These tubes provide per-timestep covariance estimates that can be used both as predicted occupancy for moving agents and as adaptive exploration guides for robot control. We evaluate SCOPE with mode-conditioned multimodal diffusion backbones in pedestrian forecasting, crowd navigation, Maze2D control, and real-world Franka Panda manipulation. Across these settings, SCOPE provides fast uncertainty estimation, which leads to better closed-loop performance. Project page: https://zackaxue.github.io/SCOPE-project-page/

cs.RO↗
arXiv2026

WorldSonus: Bringing Sound to Worlds

Recent advances in world models have enabled increasingly realistic visual synthesis. However, these generated environments remain largely silent. Bringing sound to world models poses three core challenges: real-time generation to keep pace with interactive video streams, interactive control to respond to mid-stream sound instructions, and spatially aligned stereo to reflect scene geometry and camera motion. To address these demands, we introduce WorldSonus, an interactive video-to-audio framework designed for real-time spatial sound synthesis in world models. For real-time generation, WorldSonus employs a streaming causal autoregressive diffusion architecture that synthesizes audio chunks at a low real-time factor (RTF) of 0.41. For interactive control, we incorporate an audio-centric captioning pipeline with chunk-indexed prompt scheduling, enabling dynamic manipulation of sound events during generation. For spatial alignment, we leverage high-quality stereo supervision curated from diverse stereo and ambisonic data. Extensive experiments demonstrate that while tailored for world models, WorldSonus generalizes effectively to open-domain video-to-audio benchmarks, matching or outperforming state-of-the-art bidirectional models in both acoustic quality and spatial alignment. Project page: https://noizai.github.io/WorldSonus/

cs.SD↗
arXiv2026

Change-Robust Online Topological Memory for Long-Term Relocalization and Semantic Navigation

Long-term semantic navigation requires a robot to reuse past observations after appearance and scene change, but semantic memories are only useful if the robot can relocalize into the memory without corrupting it with false visual matches. We propose CROSS, a change-robust topological memory that introduces a pre-commitment localization layer between visual place recognition and map update. Instead of treating a retrieved keyframe as an immediate place association or loop-closure factor, CROSS lifts each RGB-D retrieval into a candidate global SE(3) pose mode using relative pose estimation. A bounded Gaussian-mixture filter then propagates competing continuous trajectory branches with odometry, rejects branches that are physically inconsistent, and promotes only persistent branches to loop closures. This moves ambiguity handling from discrete place IDs or post-hoc graph-factor rejection to continuous pose-space validation before map commitment. Across public long-term relocalization benchmarks and real quadruped object-navigation experiments, CROSS improves reuse of a single sparse RGB-D memory under illumination, seasonal, dynamic-scene, and object-level change. Project page: https://jiaming.im/CROSS/

cs.RO↗
arXiv2026

Retrospective Open-Vocabulary Memory for Long-Term Object Search

Long-term object search requires learning where objects usually appear from repeated but uneven observations of a changing environment. We formulate retrospective open-vocabulary memory as probabilistic inference from censored observations, where the key idea is to reason with evidence per opportunity: a detection or non-detection should influence belief only in proportion to the robot's opportunity to observe the corresponding location. We introduce ECROM, which uses this principle to estimate long-term prevalence for concepts specified only at query time and converts the resulting belief directly into an active-search prior. To evaluate this problem, we introduce a controlled long-term benchmark in ten HM3D homes that independently varies object placement and observation opportunity across repeated traversals. ECROM improves support-level AP on held-out queries by 4.5 points and search SPL by 4.2 points over the strongest competing memory in each metric. The benchmark, dataset, and code will be open-sourced. Project page: https://jiaming.im/ecrom/

cs.RO↗
arXiv2026

Learning Social Navigation from Internet Videos in the Policy State Space

Training robust social-navigation policies requires simulators with diverse scene layouts, terrain, and human motion, but constructing such environments and specifying pedestrian behavior is costly. We propose an efficient pipeline that converts ordinary monocular walking videos directly into closed-loop social-navigation training environments in the policy's state space. Our key observation is that local social navigation primarily depends on two types of information: where the robot can traverse and how nearby pedestrians move. We therefore represent the static scene as a metric traversability map, which can be rigidly transformed under counterfactual robot motion, while directly replaying the pedestrian trajectories recovered from the video over time. This abstraction allows us to define the forward dynamics directly in the policy's state space and efficiently simulate counterfactual robot states without reconstructing or rendering photorealistic observations. The resulting policy achieves 81.2% success in the independent Arena benchmark, compared with 75.0% for the strongest baseline, and succeeds in 19/20 real-robot trials without policy fine-tuning. Project page: https://jiaming.im/VideoSocNav

cs.RO↗
arXiv2026

HelixWorld: A Real-time Interactive Audio-Visual World Model

World simulation is inherently multisensory, demanding synchronized visual and acoustic dynamics in real time. Yet prevailing interactive world models remain strictly silent, focusing exclusively on visual rendering and control while overlooking the acoustic dimension. We present HelixWorld, a real-time interactive audio-visual world model where visual scenes and camera-grounded spatial stereo sound co-evolve natively under user interaction. We curate a high-fidelity spatial audio-visual dataset with true stereo acoustics and metric camera poses, upon which we pre-train a bidirectional teacher conditioned on 6-DoF camera trajectories and user actions. To enable low-latency causal interaction, we distill the teacher into a few-step streaming student via an online trajectory distillation loss, sustaining drift-free joint audio-visual rollouts at 24 FPS on a single GPU. Furthermore, we formalize spatial-acoustic consistency and introduce HelixBench to evaluate whether synthesized sound fields faithfully track dynamic viewpoint motion. Extensive experiments demonstrate that HelixWorld matches state-of-the-art silent world models in visual fidelity and responsiveness, while significantly surpassing existing baselines in camera-aligned spatial-acoustic immersion.

cs.CV↗
arXiv2026

YuE2: Unifying Symbolic and Audio Music Generation at Frontier Quality

Symbolic models make melody, harmony, rhythm, and form explicit but typically stop before a finished recording; audio models produce complete songs while leaving composition implicit. We introduce YuE2, which unifies symbolic and audio music generation at frontier quality through symbolic planning. A single AR-NAR Mixture-of-Transformers (MoT) first writes a readable score specifying melody and harmony, expands it into semantic music tokens, and realizes it as full-song audio. In comparisons using the same checkpoint, experts prefer symbolic planning for overall quality and musicality, with 49.3% of overall preferences versus 34.6% without planning. Experts also favor the unified model over a separate language model and diffusion Transformer. On WildSongBench, YuE2 scores 6.73 on SongBench Global Avg, exceeding all evaluated public baselines. Selecting from eight candidates (best-of-8), YuE2 reaches 6.96, the highest observed mean among all evaluated systems. Expert listening further establishes its competitiveness with proprietary song generators, favoring best-of-8 over Suno v4.5 and yielding nearly balanced preferences against Suno v5. To learn this generation process from recordings without aligned scores, we introduce MERT2 and SheetSage2 to supply semantic and symbolic supervision. MERT2 sets a new state of the art in music representation learning, surpassing previous best results on 14 of 15 MARBLE metrics; SheetSage2 leads 12 of 15 benchmark-metric pairs in our lead-sheet transcription comparison. The same checkpoint follows score edits while largely preserving unedited musical content and generates zero-shot covers without cover-specific training. Its readable score also enables agentic music editing, with external language models translating user feedback into revisions of the composition.

eess.AS↗
arXiv2026

What Stops Recursive Self-Improvement in Robotics? Lessons from 123 Rounds of Agentic Skill Discovery

Can a robot improve itself the way coding agents now improve software? We built an agentic system to find out. It watches a robot fail, works out which capability is missing, writes new skills or finds and installs external models, tests every change in simulation, and repeats, with no human writing robot code. We ran it for 123 improvement rounds on household manipulation tasks. This report describes what we learned. The good news is that the agent can discover capabilities on its own: noticing that its targets were out of view, it asked for an active-viewing model, debugged it, and deployed a working search skill. The bad news is that its improvements did not add up. Changes kept passing their tests, yet the target task, putting condiments on the top shelf of a fridge, never succeeded. We found that the agent was rarely the bottleneck. Three things around it were. First, chained perception modules do not understand relations. Segmenters such as SAM 3 find shelves but not "the top shelf", so the agent filled the gap with ever more geometric rules that never converged, when what it needed was a different kind of model. Second, skill chains lock learning onto the first step. Long tasks mostly fail early, so evidence and fixes pile up there, and later skills are rarely reached, tested, or improved. Third, what the agent learns is decided by the harness. The agent optimized exactly what the evaluator measured, including where it was wrong, and weak tests and misleading memory turned activity into a standstill. We distill these lessons into concrete recommendations for building robot systems that improve themselves, each paired with an experiment that could prove it wrong.

cs.RO↗
arXiv2026

HappyWorld-Bench

Evaluating world models requires assessing both the quality of the worlds they generate and their consistency and responsiveness under exploration, interaction, and modification. We introduce HappyWorld-Bench, a comprehensive benchmark that evaluates whether generated worlds remain reliable as agents interact with them. Our design is built on a hierarchical capability framework of six world capabilities (W1-W6), from generative construction to unified world modeling, instantiated across three independent evaluation tracks: video world models, spatial world models, and embodied world models. HappyWorld-Bench comprises 1,138 video prompts, 300 spatial scenes, and 254 embodied test cases. Across all three tracks, we build and operate HappyWorld-Arena to organize human A/B comparisons and derive model-level Elo ratings, which complement newly designed automated metrics that capture behavioral correctness. We evaluate 14 video world models, 9 spatial systems, and 8 embodied candidates under this unified framework. Results reveal remaining reliability gaps across all three tracks: video models exhibit reduced consistency during extended rollouts and revisits, spatial models achieve at best 70.14% placement accuracy and 73.33% edit execution, and embodied models struggle to preserve state across multi-step actions and respond precisely to altered action conditions and physical rules. These findings highlight the need to evaluate world models not only by visual quality, but also by state consistency and the correctness of their responses to actions and interventions.

cs.CV↗
arXiv2026

How to Better Train VLAs: Lessons Learned From the REAL-I Challenge at ICRA 2026

How can robot policies learn more effectively from a fixed demonstration budget? The first Real-world Embodied AI Learning (REAL-I) Challenge at ICRA 2026 examined this question through simulation, real-robot evaluation, and an on-site final on a shared dual-arm humanoid platform. We describe the challenge tasks, data and deployment interfaces, and competition results, then compare the approaches contributed by NUS-CLEAR, RCL-Lab, and DeepTouch AI. Their systems combined pretrained vision-language-action models and task-specific imitation policies with different strategies for data curation, staged adaptation, checkpoint selection, and action-space design. The team reports highlight the importance of adapting to the deployment environment while retaining prior capabilities, treating demonstration quality at an appropriate temporal scale, and suppressing errors in inactive robot components. They also expose the limitations of offline action-prediction metrics for forecasting closed-loop success. These observations motivate a view of fixed-data robot learning that integrates data, adaptation, evaluation, and deployment.

cs.RO↗
arXiv2026

Spatiotemporal programming via asymmetric dielectric engineering for nonvolatile 2D optoelectronics

Ambipolar two dimensional (2D) semiconductors integrated with floating-gate architectures offer a promising platform for nonvolatile, reconfigurable electronics. However, the switching between p-n and n-p junction polarities has conventionally required complex multi-gate designs, hindering the scalability and integration density. Here, we demonstrate a spatiotemporal programming strategy using a dual-floating-gate architecture with a symmetry broken tunneling dielectric. An asymmetric dielectric stack creates distinct tunneling thresholds for two floating gates, enabling a single input gate to encode spatial doping profiles in the 2D channel via defined voltage pulse sequences. We achieve on demand, nonvolatile, and reversible switching between p-n and n-p configurations with excellent retention and endurance. The reconfigurable homojunction serves as a multifunctional platform for logic encoding, rectification, photodetection, and in sensor computing. This work establishes a design paradigm that replaces spatial input complexity with spatiotemporal programming, paving the way for high-density, multifunctional intelligent hardware.

cond-mat.mes-hall↗
arXiv2026

TOPO-Bench: An Open-Source Topological Mapping Evaluation Framework with Quantifiable Perceptual Aliasing

Topological mapping offers a compact and robust representation for navigation, but progress in the field is hindered by the lack of standardized evaluation metrics, datasets, and protocols. Existing systems are assessed using different environments and criteria, preventing fair and reproducible comparisons. Moreover, a key challenge - perceptual aliasing - remains under-quantified, despite its strong influence on system performance. We address these gaps by (1) formalizing topological consistency as the fundamental property of topological maps and showing that localization accuracy provides an efficient and interpretable surrogate metric, and (2) proposing the first quantitative measure of dataset ambiguity to enable fair comparisons across environments. To support this protocol, we curate a diverse benchmark dataset with calibrated ambiguity levels, implement and release deep-learned baseline systems, and evaluate them alongside classical methods. Our experiments and analysis yield new insights into the limitations of current approaches under perceptual aliasing. All datasets, baselines, and evaluation tools are fully open-sourced to foster consistent and reproducible research in topological mapping.

cs.CV↗
arXiv2026

Force-Aware Reinforcement Learning with Hybrid Sensorless Force Estimation for Wheeled-Legged Loco-Manipulation

Force-controlled loco-manipulation requires a whole-body policy to coordinate locomotion and arm motion while regulating end-effector interaction forces. This is challenging under floating-base dynamics and changing support contacts, particularly when end-effector force/torque sensing is unavailable for control. This paper presents a force-aware reinforcement learning approach with hybrid sensorless force estimation for wheeled-legged loco-manipulation. The proposed method provides a structured estimate of the end-effector force as an explicit policy observation, enabling force-guided contact behavior without using an end-effector force/torque sensor for control. The force estimate is obtained by combining generalized momentum observation, contact-constrained wrench projection, and temporal residual learning: the model-based components extract the physically structured part of the whole-body disturbance, while the residual network compensates the remaining motion-dependent bias. The estimated force is integrated into a mode-conditioned whole-body policy with an axis-wise force/position selector, allowing free-space motion, pure force regulation, and hybrid force/position control within one controller. Simulation results demonstrate improved sensorless force estimation and force-control performance. Hardware experiments further validate the proposed controller through quantitative valve-rotation and hybrid wiping evaluations, together with force-guided door opening and zero-force human-guided motion on a real wheeled-legged platform.

cs.RO↗