RRepoGEO

REPOGEO REPORT · LITE

dreamzero0/dreamzero

Default branch main · commit ab790c19 · scanned 6/22/2026, 2:47:39 AM

GitHub: 2,295 stars · 195 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface dreamzero0/dreamzero, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    robotics, zero-shot-learning, world-action-models, reinforcement-learning, deep-learning, computer-vision, nvidia, research, robot-learning, generalist-robot
  • highreadme#2
    Strengthen the README's initial positioning statement

    Why:

    CURRENT
    DreamZero is a World Action Model that jointly predicts actions and videos, achieving strong zero-shot performance on unseen tasks.
    COPY-PASTE FIX
    DreamZero is an open-source research project from NVIDIA GEAR Lab, providing code for World Action Models (WAMs) that enable zero-shot robot policies. It jointly predicts actions and videos, achieving strong zero-shot performance on unseen tasks in robotics.
  • mediumreadme#3
    Add a dedicated "Project Overview" section to the README

    Why:

    COPY-PASTE FIX
    ## Project Overview
DreamZero is a novel World Action Model (WAM) developed by NVIDIA GEAR Lab, designed to empower robots with zero-shot policy capabilities. It achieves this by jointly predicting actions and future video frames, allowing for robust performance on tasks never explicitly trained on. This repository provides the full codebase for pretraining, fine-tuning, and evaluating DreamZero models, including tools for both simulated and real-world robotic applications.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface dreamzero0/dreamzero
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI's CLIP
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI's CLIP · recommended 1×
  2. Google's RT-1 / RT-2 · recommended 1×
  3. Meta's DINOv2 · recommended 1×
  4. Google's SayCan · recommended 1×
  5. Microsoft's VALL-E · recommended 1×
  • CATEGORY QUERY
    How can I implement zero-shot robot policies for unseen tasks using video models?
    you: not recommended
    AI recommended (in order):
    1. OpenAI's CLIP
    2. Google's RT-1 / RT-2
    3. Meta's DINOv2
    4. Google's SayCan
    5. Microsoft's VALL-E
    6. Open X-Embodiment Dataset

    AI recommended 6 alternatives but never named dreamzero0/dreamzero. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help with few-shot adaptation for robot language following and pick-and-place?
    you: not recommended
    AI recommended (in order):
    1. OpenVLA
    2. RoboCat
    3. RT-X
    4. Hugging Face Transformers
    5. RLBench
    6. PyTorch
    7. TensorFlow
    8. ROS
    9. MoveIt!

    AI recommended 9 alternatives but never named dreamzero0/dreamzero. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of dreamzero0/dreamzero?
    pass
    AI named dreamzero0/dreamzero explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts dreamzero0/dreamzero in production, what risks or prerequisites should they evaluate first?
    pass
    AI named dreamzero0/dreamzero explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo dreamzero0/dreamzero solve, and who is the primary audience?
    pass
    AI named dreamzero0/dreamzero explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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dreamzero0/dreamzero — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite