sail-sg

sail-sg / TeamHOI

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[CVPR 2026] TeamHOI: Learning a Unified Policy for Cooperative Human-Object Interactions with Any Team Size

splionar.github.ioTeamHOI character-animation cooperative-ai embodied-ai human-motion-generation human-object-interaction
19
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100% credibility
Found Mar 14, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

TeamHOI is a research project that trains AI agents to cooperatively lift and transport objects using teams of varying sizes in a physics simulation.

How It Works

1
👀 Discover TeamHOI

You find this exciting research project on GitHub, where AI characters learn to team up and carry heavy objects together.

2
🛠️ Prepare your computer

You create a simple workspace on your machine to keep everything organized and ready.

3
📥 Add simulation tools

You download special tools that let virtual characters move realistically in a physics world.

4
🚀 Watch agents in action

With one command, you launch a test and see teams of characters smoothly lift and carry tables side by side.

5
Grow your team
🔍
Test different teams

Try teams of 2, 4, or 8 characters carrying various objects.

🎓
Train smarter agents

Guide them to master lifting and moving with your own examples.

🎉 Perfect teamwork!

Your characters now cooperate flawlessly, lifting and transporting anything as a flexible team.

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AI-Generated Review

What is TeamHOI?

TeamHOI trains decentralized RL policies for humanoid agents to cooperatively lift and transport heavy objects like tables, scaling seamlessly from 2 to 8 agents without retraining. Built in Python with Isaac Gym for fast physics sims, it uses pretrained AMP models and transformer-based policies to handle variable team sizes and object shapes in a single unified setup. Users get CLI commands like `python run.py --test` for inference on checkpoints, plus staged training pipelines for walk-lift-transport tasks.

Why is it gaining traction?

Unlike fixed-team RL baselines, TeamHOI's transformer architecture enables zero-shot generalization to new team sizes, making multi-agent coordination practical without per-size finetuning. Early buzz on cvpr 2026 reddit and github cvpr 2026 threads highlights its clean Isaac Gym integration and motion retargeting from mocap data, letting devs prototype cooperative HOI faster than from-scratch setups. Ties into cvpr 2026 papers github trends, with arXiv preprint and project page for quick experiments.

Who should use this?

Multi-agent RL researchers simulating embodied teamwork, robotics devs benchmarking scalable policies in Isaac Gym, or CVPR 2026 workshop attendees replicating team hours transport tasks. Ideal for academics chasing cvpr 2026 deadline extensions or exploring cvpr github template for policy papers.

Verdict

Promising for cvpr 2026 accepted papers code but immature at 19 stars and 1.0% credibility—docs are README-focused with no tests, so expect setup tweaks for Isaac Gym. Grab it now for research prototypes if you're in multi-agent sims; skip for production.

(198 words)

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