π_{0.5} is a VLA model that achieves long-horizon dexterous manipulation in entirely new homes through co-training on heterogeneous tasks and multi-source data including web and semantic predictions.
R3m: A universal visual representation for robot manipulation
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OpenVLA-OFT fine-tuning boosts LIBERO success rate from 76.5% to 97.1%, speeds action generation 26x, and outperforms baselines on real bimanual dexterous tasks.
Seer, a transformer-based PIDM pre-trained on large robotic datasets like DROID, outperforms prior methods on simulation and real-world robotic manipulation benchmarks with gains up to 43%.
citing papers explorer
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$\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
π_{0.5} is a VLA model that achieves long-horizon dexterous manipulation in entirely new homes through co-training on heterogeneous tasks and multi-source data including web and semantic predictions.
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Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
OpenVLA-OFT fine-tuning boosts LIBERO success rate from 76.5% to 97.1%, speeds action generation 26x, and outperforms baselines on real bimanual dexterous tasks.
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Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation
Seer, a transformer-based PIDM pre-trained on large robotic datasets like DROID, outperforms prior methods on simulation and real-world robotic manipulation benchmarks with gains up to 43%.