ErickRosete/tacorl
TACO-RL: Latent Plans for Task-Agnostic Offline Reinforcement Learning
TACO-RL helps roboticists and automation engineers teach robots many different tasks efficiently by leveraging large amounts of previously collected, unorganized robot demonstration data. It takes raw, unlabeled video and robot movement data as input and produces a versatile robot control policy that can perform various complex actions in the real world or simulations. This is for researchers and engineers developing general-purpose robots or automated systems.
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Use this if you need to train a robot to perform a wide variety of tasks using existing, diverse, uncurated offline datasets, rather than collecting new data for each specific task.
Not ideal if you have very little existing offline data or if your robot only needs to perform a single, highly specific, and unchanging task.
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30
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2
Language
Python
License
MIT
Category
Last pushed
Jan 26, 2023
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