xf-zhao/Agentic-Skill-Discovery
Official implementation of Zero-Hero paper
This project helps robotics engineers and researchers rapidly develop and test new robot behaviors for complex tasks. By providing a high-level description of a desired task and the robot's environment, it automatically generates specific sub-tasks and trains reinforcement learning policies to execute them. The output is a robust, learned robot policy that can perform the described actions within a simulated environment.
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Use this if you need to quickly teach robots new skills for a variety of tasks in a simulated environment, using natural language descriptions.
Not ideal if you are looking for a solution for real-world robot deployment without prior simulation or if you require fine-grained, manual control over every aspect of policy development.
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Python
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Last pushed
Feb 13, 2025
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