H-EmbodVis

H-EmbodVis / DOMINO

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Towards Generalizable Robotic Manipulation in Dynamic Environments

h-embodvis.github.ioDOMINO
50
0
100% credibility
Found Mar 25, 2026 at 49 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

DOMINO is a benchmark and simulation platform for developing and evaluating robots that manipulate objects in dynamic, changing environments.

How It Works

1
🔍 Discover DOMINO

You hear about DOMINO, a fun playground for teaching robots to handle moving objects like a pro.

2
🚀 Set up your robot world

With a few simple steps, you prepare a virtual space where robots practice real-life surprises.

3
📹 Record smart robot moves

Capture videos of expert robots grabbing, placing, and adapting to objects that wiggle or roll away.

4
🤖 Watch robots shine in chaos

See your robot buddies react lightning-fast to dynamic changes, nailing tasks that static ones fumble.

5
🧠 Test clever robot thinkers

Put advanced robot brains to the test on tough moving-object challenges.

🎉 Master dynamic robot magic

Celebrate as your robots conquer unpredictable environments, ready for the real world!

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Star Growth

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AI-Generated Review

What is DOMINO?

DOMINO builds a Python simulation benchmark for robotic manipulation where objects move dynamically, tackling VLA models' weakness on static single-frame data. You get 35 tasks across hierarchies, 110K+ expert trajectories in HDF5, and tools to collect data via simple scripts like `collect_data.sh` or evaluate policies with `eval.sh`. Paired with NVIDIA GPUs on Linux, it generates dynamic scenes via configs for moving targets and domain randomization.

Why is it gaining traction?

Unlike static robotics sims or distractions like domino pizza coupons and dominosteine rezept, DOMINO hooks devs with predictive PUMA architecture blending optical flow and future-state queries for reactive control—boosting success 6.3% over baselines. Its multi-dimensional metrics (Success Rate + Manipulation Score) and seamless RoboTwin integration make benchmarking VLAs straightforward, drawing robotics folks beyond basic domino ai github toys.

Who should use this?

Robotics researchers benchmarking VLAs on dynamic pick-place or handover tasks with moving clutter. VLA trainers needing scalable data pipelines for spatiotemporal generalization, especially those extending DexVLA or ACT policies.

Verdict

Grab it if you're into towards ai github robotics—strong foundation for dynamic benchmarks, but 47 stars and 1.0% credibility reflect early maturity with pending dataset, PUMA code, and checkpoints. Fork and contribute to accelerate.

(198 words)

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