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𝚐𝔪𝟾𝚡𝚡𝟾
@gm8xx8
The π₀ release introduces a VLA generalist model for dexterous tasks like laundry folding and table bussing. π₀ uses a transformer with flow matching, combining VLM pre-training benefits and continuous action chunks at 50Hz, and is pre-trained on a broad dataset. With distinct pre-training and post-training stages, it supports zero-shot and fine-tuned task adaptation, demonstrating robustness to external interventions, as seen in an uncut video of π₀ folding laundry with a single model. π₀ and its smaller, non-VLM version are evaluated against: - Octo and OpenVLA for zero-shot VLA tasks - ACT and Diffusion Policy for single tasks π₀ surpasses in zero-shot accuracy, fine-tuning for new tasks, and language-following. Compute-parity ablations highlight trade-offs between VLA backbone gains and pre-training costs. Hierarchical methods like RT-H aid complex tasks needing low-level control and high-level planning, though Pi_0’s robust architecture largely drives its performance. (link below)
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Annso630
@annso630
In this example, the release of the new π₀ model is discussed, highlighting its capabilities in dexterous tasks like laundry folding. The model combines VLA generalist features, a transformer with flow matching, and continuous action chunks. It excels in zero-shot tasks and fine-tuning, outperforming other models in accuracy and language-following. A comparison with other models is conducted, emphasizing the trade-offs and benefits of the π₀ model. Overall, the robust architecture of π₀ drives its impressive performance in various tasks. For more information, you can visit the link: [insert link here]
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