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Researcher profile

Yi Ren

Yi Ren contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
astro-ph.GAastro-ph.SRRoboticsArtificial Intelligence

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Published work

4 published item(s)

preprint2026arXiv

An Enhanced Sample of Galactic Red Supergiants Reveals Spiral Structures

Red supergiants (RSGs), representing a kind of massive young stellar population, have rarely been used to probe the structure of the Milky Way, mainly due to the long-standing scarcity of Galactic RSG samples. The Gaia BP/RP spectra (hereafter XP), which cover a broad wavelength range, provide a powerful tool for identifying RSGs. In this work, we develop a feedforward neural network classifier that assigns to each XP spectrum a probability of being an RSG, denoted as $\mathrm{P(RSG)}$. We perform ten independent runs with randomly divided training and validation sets, and apply each run to all XP spectra of stars with $G < 12$ mag. By selecting sources with $\mathrm{P(RSG)} \geq 0.9$, ten high-confidence candidate samples are obtained. A star is considered a ture Galactic RSG only if it appears in at least eight of these samples, yielding a final catalog of 2,436 objects. These RSGs show a clear spatial correlation with OB stars and trace the Galactic spiral arms well, confirming the reliability of our classification, and highlighting their potential to serve as powerful tracers of the Milky Way&#39;s structure.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

BiKC+: Bimanual Hierarchical Imitation with Keypose-Conditioned Coordination-Aware Consistency Policies

Robots are essential in industrial manufacturing due to their reliability and efficiency. They excel in performing simple and repetitive unimanual tasks but still face challenges with bimanual manipulation. This difficulty arises from the complexities of coordinating dual arms and handling multi-stage processes. Recent integration of generative models into imitation learning (IL) has made progress in tackling specific challenges. However, few approaches explicitly consider the multi-stage nature of bimanual tasks while also emphasizing the importance of inference speed. In multi-stage tasks, failures or delays at any stage can cascade over time, impacting the success and efficiency of subsequent sub-stages and ultimately hindering overall task performance. In this paper, we propose a novel keypose-conditioned coordination-aware consistency policy tailored for bimanual manipulation. Our framework instantiates hierarchical imitation learning with a high-level keypose predictor and a low-level trajectory generator. The predicted keyposes serve as sub-goals for trajectory generation, indicating targets for individual sub-stages. The trajectory generator is formulated as a consistency model, generating action sequences based on historical observations and predicted keyposes in a single inference step. In particular, we devise an innovative approach for identifying bimanual keyposes, considering both robot-centric action features and task-centric operation styles. Simulation and real-world experiments illustrate that our approach significantly outperforms baseline methods in terms of success rates and operational efficiency. Implementation codes can be found at https://github.com/JoanaHXU/BiKC-plus.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Discovery of an Extremely Luminous Type II Cepheid in the Andromeda Giant Stellar Stream: Evidence for a Hierarchical Triple with an Inner Binary Merger

We report the discovery of LAMOST J0041+3948, the most luminous post-AGB Type II Cepheid (TIIC) known, located in the Andromeda Giant Stellar Stream. Its spectral energy distribution (SED) exhibits a strong near-infrared excess, indicating the presence of a circumbinary dusty disk and hence binarity. SED fitting yields an effective temperature of $T_{\rm eff}=6738_{-262}^{+234}\,$K and a post-AGB luminosity of $\log(L/L_{\odot})=4.32_{-0.08}^{+0.07}$. Comparison with theoretical evolutionary tracks suggests a ~$2.0$-$4.0\,M_{\odot}$ progenitor when accounting for a possible scattered-light contribution. ZTF Light curves reveal a pulsation period of 89d that lies close to the period-luminosity relation for long-period RV Tauri stars. Follow-up spectroscopy reveals clear $s$-process enrichment and signatures consistent with an accretion disk around the companion. The inferred progenitor is significantly younger and more massive than a typical stream member, suggesting that an additional mechanism such as a stellar merger is required. We propose a formation channel in which the present post-AGB binary descends from a hierarchical triple system. In this scenario, the inner binary merged after the system was displaced to its current location by the galaxy merger event, and the resulting massive merger remnant subsequently evolved into the extremely luminous post-AGB star observed today.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

HoloMotion-1 Technical Report

In this report, we present HoloMotion-1, a humanoid motion foundation model for zero-shot whole-body motion tracking. A key innovation of HoloMotion-1 is to scale control-policy training with a large-scale hybrid motion corpus, where video-reconstructed motions from in-the-wild videos provide the dominant source of motion diversity, while curated motion-capture and in-house motion data provide higher-fidelity supervision and deployment-oriented coverage. This data regime enables HoloMotion-1 to move beyond conventional MoCap-only training and exposes the policy to substantially broader behaviors, capture conditions, and motion styles. Learning from such heterogeneous data introduces new challenges, including reconstruction noise, source-domain mismatch, uneven motion quality, and the need for temporal modeling under large behavioral variation. To address these challenges, HoloMotion-1 integrates large-capacity temporal modeling, a sparsely activated Mixture-of-Experts Transformer with KV-cache inference for real-time control, and a sequence-level training strategy that improves learning efficiency on extended motion sequences. Extensive experiments on multiple unseen motion benchmarks show that HoloMotion-1 generalizes robustly across diverse motion types and capture conditions, significantly improves tracking accuracy over prior methods, and transfers directly to a real humanoid robot without task-specific fine-tuning.

0 citations0 reviewsNo code linkedOpen access