mees/calvin
CALVIN - A benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks
This project provides a standardized way to train and evaluate AI models for complex robot manipulation. It takes human language instructions, along with various sensor inputs like camera images or tactile data, and helps create robotic agents that can perform long sequences of actions, like opening a drawer and placing an object inside. It's designed for robotics researchers and engineers who are developing AI for practical, language-guided robot tasks.
854 stars. No commits in the last 6 months.
Use this if you are a robotics researcher working on developing AI agents that can understand and execute multi-step robot manipulation tasks based on natural language commands.
Not ideal if you need a plug-and-play solution for immediate robot deployment, as this is a benchmark for developing and testing new AI policy learning methods.
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854
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111
Language
Python
License
MIT
Category
Last pushed
Sep 08, 2025
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