xxm19

xxm19 / hommi

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HoMMI: Learning Whole-Body Mobile Manipulation from Human Demonstrations

25
0
100% credibility
Found Mar 11, 2026 at 20 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

A GitHub repository for an academic research project on enabling robots to learn complex whole-body movements and manipulation tasks from human video demonstrations, with code release forthcoming.

How It Works

1
🔍 Stumble upon HoMMI

While browsing for cool robot videos or research, you discover this project about robots learning from watching people.

2
🏠 Visit the project page

You head to the main hub to see who's behind it – a team from Stanford University and Toyota.

3
💡 Grasp the big idea

You get excited learning how robots can mimic full-body human movements like walking and grabbing just from demonstrations.

4
📄 Explore more details

You click through to the project website and research paper to dive into the fascinating story.

5
⭐ Stay in the loop

You follow the page so you're notified the moment everything is ready to try yourself.

🚀 Start experimenting

The tools arrive, and you can now play with teaching robots new skills from your own videos!

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Star Growth

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AI-Generated Review

What is hommi?

HoMMI trains robots for whole-body mobile manipulation by imitating human demonstrations, tackling the challenge of coordinating locomotion and arm actions in real-world tasks like picking up objects while moving. Developers get tools to replicate complex behaviors from video or teleop data, bridging the gap between static manipulators and dynamic mobile robots. Built on standard ML frameworks with unknown primary language, it emphasizes end-to-end learning from demos without manual trajectory engineering.

Why is it gaining traction?

It stands out by handling full-body dynamics—think homming a heavy load across uneven terrain—unlike siloed approaches in hommi reitrock or hommix libraries that ignore mobility. The hook is plug-and-play imitation for hommis and hommik anuga setups, slashing sim-to-real transfer time compared to traditional RL methods. Early adopters praise its focus on human-like fluidity over brittle scripted motions.

Who should use this?

Robotics engineers at labs like Stanford or Toyota building humanoid or wheeled manipulators for warehouses. Imitation learning specialists tired of hand-crafting policies for tasks involving hommingberger gepardenforelle-style agile grasping. Mobile robot devs integrating whole-body control in hommie or hommis platforms.

Verdict

Skip for now—1.0% credibility score reflects just a README with code pending internal review, 19 stars, and thin docs. Watchlist it post-release for credible whole-body demos, but prototype alternatives until maturity catches up.

(178 words)

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