# BAAI presents CausGround, a causal planner that guides real robots without retraining

> Original title: Can simulation-distilled causal knowledge directly guide real robots without retraining?

- Source: BAAI
- Published: 2026-11-05T13:46:00.000Z
- Bad Influence: https://badinfluence.news/items/fo38dp8b7iwzbm4xkr5cq4xzc
- Original: https://x.com/BAAIBeijing/status/2118339048788156655

## Summary

BAAI presents CausGround, which uses simulation-distilled causal knowledge to guide real dual-arm robots without retraining. In the system, a frozen MLLM interprets natural language into a goal state, and CausGround reasons backward through a causal graph to generate valid action sequences, such as Open(Microwave) ➔ Pick(Bread) ➔ Place(Bread, Plate).

## Post

Can simulation-distilled causal knowledge directly guide real robots without retraining?

BAAI‘s answer from CausGround is YES.

In real-world dual-arm manipulation tasks, the frozen MLLM only interprets natural language into a goal state. CausGround reasons backward through the causal graph to automatically generate valid sequences—like Open(Microwave) ➔ Pick(Bread) ➔ Place(Bread, Plate)!

Explore our interactive demos, benchmark, and paper:
🌐 Project:https://causground.github.io
📄 arXiv： https://arxiv.org/abs/2610.29702

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