StanfordVL/GibsonEnv
Gibson Environments: Real-World Perception for Embodied Agents
This project offers a realistic virtual environment for training AI models in embodied perception and sensorimotor control. It takes real-world 3D spaces as input and simulates an agent's interaction within them, outputting improved AI models capable of complex visual understanding and physical navigation. This tool is designed for AI researchers and robotics engineers developing active perception systems.
936 stars. No commits in the last 6 months.
Use this if you need to efficiently train AI agents to perceive and move realistically within complex, real-world environments without the fragility and cost of physical robots.
Not ideal if your primary goal is developing AI for tasks that don't involve physical embodiment or require learning solely from abstract data representations.
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936
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149
Language
C
License
MIT
Category
Last pushed
Apr 15, 2024
Commits (30d)
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