DeCLaRe Lab logo

DeCLaRe Lab

Research labSingapore

Academic research lab whose robotics work includes the NORA family of open vision-language-action models.

Overview

The Deep Cognition and Language Research (DeCLaRe) Lab is an academic research group based in Singapore. Its NORA paper (April 2025) lists authors at the Singapore University of Technology and Design, and the NORA-1.5 paper (November 2025) lists authors at Nanyang Technological University. The lab released NORA, a 3B-parameter VLA built on Qwen-2.5-VL-3B, and NORA-1.5, which adds a flow-matching action expert.

Sources: github.com (19 Oct 2026) · arxiv.org (28 Apr 2025) · arxiv.org (18 Nov 2025)

Type
Research lab
Headquarters
Singaporegithub.com, 19 Oct 2026

Key people

  • Soujanya PoriaSenior author, NORAarxiv.org, 28 Apr 2025
  • Navonil MajumderAuthor, NORA and NORA-1.5arxiv.org, 18 Nov 2025
  • Chia-Yu HungLead author, NORA and NORA-1.5arxiv.org, 18 Nov 2025

Funding history

Research lab · Academic research lab; no separate funding disclosed

Ranking

#14 of 104 model companies · score 75.8 / 100 · updated 19 Oct 2026

  • Published benchmarks 35%93%
  • Open weights 20%100%
  • Adoption 20%49%
  • Released models 15%40%
  • Disclosed funding 10%n/a

Each bar shows an input from 0 to 100% before weighting. 2 of 2 listed models have open weights. Most-starred official repository: declare-lab/nora (225 stars, 19 Oct 2026).

How we rank model companies

The score (0–100) measures public, checkable evidence about a company's robot models. It does not measure private capability. A company that publishes no benchmark results and no weights can still have strong models. Every input is computed from the data on each company page, and every number there links to its source.

InputWeightHow it is measured
Published benchmarks35%For each scored benchmark, the company's best result divided by the best result any company has published, averaged over the benchmarks it reports. No published results scores 0. Results come only from the model's own paper, model card or the official benchmark leaderboard. Scored: LIBERO (4-suite average success, %); SimplerEnv Google Robot, visual matching (%); SimplerEnv Google Robot, variant aggregation (%); SimplerEnv WidowX / Bridge (%); CALVIN ABC→D (average length, 0–5); RoboArena (pairwise rating).
Open weights20%Share of the company's listed models whose weights the public can download.
Adoption20%GitHub stars on the company's most-starred official model repository, on a log scale relative to the most-starred repository in the list. Hugging Face downloads are shown on company pages but not scored, because gated repositories report zero.
Released models15%Number of publicly announced, named models, capped at 5.
Disclosed funding10%The sum of the funding rounds listed on the company's profile that were disclosed in USD, on a log scale from $10M to the largest sum in the list. Rounds reported only in other currencies are listed but not converted or counted. This input applies only to private startups and robot makers. For big-tech divisions, research labs and listed companies it is n/a, and its weight is shared across the other inputs in proportion.

Sources. arXiv papers and official model cards for benchmarks and parameters; official leaderboards (RoboArena); the GitHub and Hugging Face APIs for stars and downloads; company announcements and established press for funding. Missing values are shown as n/a, never estimated.

Updated 19 Oct 2026. Benchmark protocols differ between papers, for example in fine-tuning and number of trials, so small gaps between close scores are not meaningful.

Submit or correct data →

Open-weights models (2)

DeCLaRe Lab logo

NORA-1.5

Open weightsVLA

VLA built from the pre-trained NORA backbone with an added flow-matching action expert, post-trained with DPO using world-model and action-based rewards.

Released
18 Nov 2025
Parameters
n/a
License
MIT
Embodiments
single-arm
GitHub stars
109 as of 19 Oct 2026
HF downloads (all time)
233 as of 19 Oct 2026
Published benchmark results for NORA-1.5
BenchmarkResultSource
LIBEROAverage success rate across LIBERO-Spatial/Object/Goal/Long (%)94.5%arxiv.orgLIBERO table, NORA-1.5 (without DPO); NORA-1.5 (DPO) 95.0. As of 18 Nov 2025
SimplerEnv Google VMSimplerEnv Google Robot visual matching average (%)77.9%arxiv.orgTable 1, NORA-1.5 fine-tuned. As of 18 Nov 2025
SimplerEnv Google VASimplerEnv Google Robot variant aggregation average (%)70.7%arxiv.orgTable 1, NORA-1.5 fine-tuned. As of 18 Nov 2025
DeCLaRe Lab logo

NORA

Open weightsVLA

3B-parameter VLA using a Qwen-2.5-VL-3B backbone and the FAST+ action tokenizer, trained on 970k real-world robot demonstrations.

Released
28 Apr 2025
Parameters
3B
License
n/a
Embodiments
single-arm
GitHub stars
225 as of 19 Oct 2026
HF downloads (all time)
39.9k as of 19 Oct 2026
Published benchmark results for NORA
BenchmarkResultSource
LIBEROAverage success rate across LIBERO-Spatial/Object/Goal/Long (%)73.9%arxiv.orgTable 2, NORA fine-tuned (no action chunking); NORA-fine-tuned-AC 79.5, NORA-Long fine-tuned 87.9. As of 28 Apr 2025