Robot-learning researcher focused on decision-aligned latent world models and robust VLA calibration for manipulation
2
papers here
2
reviewed by Evolve
2
first-author
0
corresponding
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works · OpenAlex
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h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-10-13) and cover the author's entire public record, not just the papers listed here. Reproducibility signal comes from Evolve's own reviews.
Research profile
Output—
Not yet scored. This axis would measure research output volume and consistency — publication counts, venues, and first/corresponding-author share over time — but the current snapshot contains no aggregate beyond the two reviewed 2026 arXiv preprints.
Impact—
Not yet scored. The impact axis would measure citation-based influence — citation counts, aggregate indices, and uptake by other work — but no such aggregates are available here, and both 2026 preprints are too recent for meaningful citation accumulation.
Focus0/100
The focus axis scores 0, derived from a field distribution split exactly 0.5/0.5 between 'robotics / embodied machine learning' and 'robot learning / world models'. The two reviewed 2026 papers each sit in one of these adjacent areas — D-JEPA on world models, BAS-VLA on embodied learning — so current output spreads evenly across two labels rather than concentrating in a single field.
Collaboration55/100
The collaboration axis scores 55 from a raw count of 15 distinct coauthors. The recurring names are Feiyang You and Chengyu Wu, each listed twice, alongside Shuyang Hao, Chenglong Zhang, Ningxin Su, Jingyao Cai, Xingwei Chen, Qifu Wen, and Xi Lin. This indicates a moderately sized, repeat-collaborator core supporting both 2026 first-author papers, rather than a solo effort or a dispersed one-off network.
Activity—
Not yet scored. The activity axis would track recent publishing cadence — papers per year and recency of contributions — but the snapshot holds only the two 2026 arXiv preprints and no multi-year publication record, so no cadence estimate is possible.
Reproducibility55/100
Reproducibility scores 55, computed as a mean over 2 reviewed papers, both rated medium readiness. The reviews temper this: D-JEPA reports consistent simulation and real-robot gains but releases no code, with flagged cohort selection and a tiny driving set; BAS-VLA shows solid simulations under appearance shift, yet its evidence for preserved capability is narrow and its semantic-change failure (0% stale success) bounds the claim.
Research summary
Shuaijun Liu is a robot-learning researcher whose reviewed record in this catalog consists of two first-author arXiv preprints, both dated 2026, split evenly across the two recorded fields — 'robotics / embodied machine learning' and 'robot learning / world models' each hold 0.5 of the field distribution. The first, D-JEPA, re-ranks candidate futures from a latent world model through a bounded set transformer to close a decision-local gap, reporting consistent gains in simulation and on real robots; Evolve's review flags cohort selection, a tiny driving set, and no released code. The second, BAS-VLA, adds a task-semantic calibration layer to frozen vision-language-action models: it remains stable under appearance shift but breaks under semantic change, with stale success falling to 0%, and its simulations are solid while preserving evidence stays narrow. Both papers are rated medium reproducibility readiness, averaging a score of 55 across the 2 reviewed papers. Collaboration data records 15 distinct coauthors, with Feiyang You and Chengyu Wu recurring most often. Output, impact, and activity dimensions remain unscored because the underlying aggregates are not yet available. No institution, country, or ORCID is attached, so this profile is name-resolved from public bibliographic data.
Collaboration pattern
The snapshot records 15 distinct coauthors across the reviewed work. Feiyang You and Chengyu Wu recur most often, each listed twice, followed by Shuyang Hao, Chenglong Zhang, Ningxin Su, Jingyao Cai, Xingwei Chen, Qifu Wen, and Xi Lin. With a collaboration score of 55, the pattern suggests a moderately sized core team supporting both 2026 first-author papers rather than solo work or a sprawling one-off network; no affiliation data is available to locate these collaborators institutionally.
Research keywords
Highlights
D-JEPA closes a decision-local gap in latent world models
BAS-VLA calibrates frozen VLA models against task-semantic change
Both 2026 first-author papers share a medium reproducibility posture
The two reviewed preprints average a reproducibility score of 55 across 2 papers, each rated medium readiness; neither ships code, and both carry evaluation caveats — cohort selection for D-JEPA and narrow preserving evidence for BAS-VLA.
Timeline
- 2026Posts D-JEPA, a decision-aligned latent world model, as a first-author arXiv preprint.
- 2026Posts BAS-VLA, a task-semantic action-calibration method for frozen VLA models, as a first-author arXiv preprint.
- 2026Both preprints enter the Evolve review catalog, each rated medium reproducibility readiness with a mean score of 55 across 2 papers.
- 2026Collaboration snapshot records 15 distinct coauthors across the reviewed work.
Papers in the Evolve catalog
Frequent co-authors
About this profile
This profile is AI-structured by Evolve.Pub from public bibliographic data (OpenAlex) and Evolve's own paper reviews. It is not written or endorsed by the researcher unless the profile is claimed. Radar dimensions are computed from real bibliometrics with fixed formulas — raw figures are shown alongside; missing dimensions mean insufficient data, never zero. Errors? Claim the profile to correct or hide it.
- Radar scores and collaboration counts are derived from OpenAlex-derived public bibliometric data only; no citation, venue, or volume aggregates were available.
- The reviewed corpus is limited to two 2026 arXiv preprints, both first-author but non-corresponding; no peer-reviewed venue record appears in the snapshot.
- No institution, country, or ORCID is attached; the profile is name-resolved from public bibliographic data and may be affected by homonymous researchers.
- The frequent-coauthor list includes the researcher's own name, an apparent self-listing artifact that was excluded from the collaborator narrative.