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README.md
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The goal is to use this to compare ACTs performance on PushT against diffusion policy particularly the aspect of action multimodality.
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[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
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The goal is to use this to compare ACTs performance on PushT against diffusion policy particularly the aspect of action multimodality.
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Training logs: https://api.wandb.ai/links/ramachandranaadarsh-indian-institute-of-technology-madras/7m9dpcgw
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[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
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