Full fine-tune of lerobot/pi05base on a bimanual actuator-unboxing task, with a speed token in the text prompt: Part of a check whether pi0.5 can be conditioned through text tokens, as a precursor to advantage conditioning (RECAP, π0.6). Data (not on the Hub): 198 teleoperated episodes (50 fps, three 224×224 cameras, 14-D state and action) labelled slow, plus two 2× copies of every episode that keep only the even or only the odd frames, labelled fast (594 episodes, 348,616 frames). Slow and fast samples show the same images, so only the token tells them apart. 30 % of training samples get unknown, the null prompt for classifier-free guidance. Training: 4,000 steps (about 3 epochs), batch…
Open weights
3.4B parameters
lerobot
NVIDIA Isaac GR00T N1.6 is an open vision-language-action (VLA) model for generalized humanoid robot skills. This cross-embodiment model takes multimodal input, including language and images, to perform manipulation tasks in diverse environments. GR00T N1.6 is trained on a diverse mixture of robot data including bimanual, semi-humanoid and an expansive humanoid dataset, consisting of real captured data, synthetic data generated using the components of NVIDIA Isaac GR00T Blueprint. It is adaptable through post-training for specific embodiments, tasks and environments. The neural network architecture of GR00T N1.6 is a combination of vision-language foundation model and diffusion transformer…
Open weights
3.3B parameters
This repository contains a checkpoint of the Pi0 model (HF implementation | Paper) finetuned on the BridgeV2 dataset for robotic manipulation tasks. The model is later used for testing on the Simpler Environment and our INTACT Probing Suite for the generalization boundaries of VLA models. Paper: From Intention to Execution: Probing the Generalization Boundaries of Vision-Language-Action Models Or directly in python with Lerobot, see blow: First, install lerobot Then For more details please refer to our paper and code Checkpoint choice After training 15 epochs, we sweep the checkpoint at epoch 1, 2, 3, 4, 5, 10, 15 for performance on the original 4 Bridge tasks in the SimplerEnv, and choose…
Open weights
apache-2.0
3.2B parameters
transformers
π₀ is a Vision-Language-Action (VLA) foundation model from Physical Intelligence that jointly reasons over vision, language, and actions to control robots, serving as the base architecture that later enabled π₀.₅’s open-world generalization. Original paper: π0: A Vision-Language-Action Flow Model for General Robot Controlion For full installation details (including optional video dependencies such as ffmpeg for torchcodec), see the official documentation: https://huggingface.co/docs/lerobot/installation If you’re training / fine-tuning, you typically call forward(...) to get a loss and then: - -policy.chunksize=... - -policy.nactionsteps=... - -policy.maxactiontokens=...…
Open weights
gemma
3.5B parameters
lerobot
NVIDIA Isaac GR00T N1.7 is an open foundation model for generalized humanoid robot reasoning and skills. This cross-embodiment model takes multimodal input, including language and images, to perform manipulation tasks in diverse environments. Developers and researchers can post-train GR00T N1.7 with real or synthetic data for their specific humanoid robot or task. Isaac GR00T N1.7 is the medium-sized version of our model built using pre-trained vision and language encoders, and uses a flow matching action transformer to model a chunk of actions conditioned on vision, language and proprioception. A detailed description of the Isaac GR00T N1.X architecture is provided in the GROOT N1 White…
Open weights
3.1B parameters
NVIDIA Isaac GR00T N1.7 is an open foundation model for generalized humanoid robot reasoning and skills. This cross-embodiment model takes multimodal input, including language and images, to perform manipulation tasks in diverse environments. Developers and researchers can post-train GR00T N1.7 with real or synthetic data for their specific humanoid robot or task. Isaac GR00T N1.7 is the medium-sized version of our model built using pre-trained vision and language encoders, and uses a flow matching action transformer to model a chunk of actions conditioned on vision, language and proprioception. A detailed description of the Isaac GR00T N1.X architecture is provided in the GROOT N1 White…
Open weights
3.1B parameters
