MolmoAct2 LIBERO MolmoAct2 is an open vision language action model for robot control. It builds on Molmo2 ER and attaches a flow matching continuous action expert that conditions on the VLM key value cache through a per layer connection. This checkpoint is fine tuned on the full LIBERO training mixture, combining Spatial, Object, Goal, and Long suites. It is intended for both further fine tuning and LIBERO policy inference. Quick Links 📂 Models: Models, Finetuned Models 📂 Datasets: MolmoAct2 BimanualYAM Dataset, MolmoAct2 Datasets, Molmo2 ER Datasets 📄 Paper: arXiv:2605.02881 💻 Code: allenai/molmoact2 🎥 Blog Post: MolmoAct2 Intended Use Use this checkpoint for LIBERO inference or for further fine tuning. Dataset normalization metadata is stored in norm stats.json . pass norm tag="libero" at inference time. Continuous action prediction is the intended and recommended inference mode. Discrete action prediction is exposed for parity and debugging, but we use continuous actions by default. Install Sample Input This sample comes from libero 10 , episode 0, frame 0. The LIBERO camera order is front/agent view followed by wrist view. Agentview RGB Wrist RGB Continuous Actions MolmoAc…
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