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
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
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
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 labelled slow, merged with 437 episodes of the same task demonstrated about 1.75× faster in another session, labelled fast (635 episodes, 371,523 frames; 50 fps, three 224×224 cameras, 14-D state and action). 30 % of training samples get unknown, the null prompt for classifier-free guidance. Training: 4,400 steps (about 3 epochs), batch 256, learning rate 5e-5, 10 % linear warm-up, cosine decay over the last…
Open weights
3.4B parameters
lerobot
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
압축 가능성 라벨로 게이트를 학습한 액션 양자화(ATQ) 체크포인트다. 하나의 정책이 미세(1x) 디코더와 압축 디코더를 함께 갖고, VLM 라벨에서 배운 conf 가 둘 중 어느 군을 쓸지 고른다. 라우터는 그 군 안에서 horizon 만 고른다. (VLM 전용, 접촉 열 없음 · 16,286행 · 1,693에피 · stride 16) moeexperthorizons = [16, 9, 5, 8] · confthreshold(tau) = 0.55 · discreteactiondims = [6] (그리퍼는 절대 명령이라 · actionmergereduction = sum 회전 병합은 SO(3) 다(rotationmergespec 이 config 에 있다). 압축 블록의 회전 다시 정규화한다. scipy 대조 각도 오차 1e-14도. LIBERO 는 5 fine 스텝마다 재계획한다. 압축 행 하나는 fine 액션 2~3개의 합이므로 같은 배속이 되고, 배속을 움직이는 손잡이는 conf 게이트 하나다: 넘으므로 OSC 팔 컨트롤러의 입력 클립을 제거한 조건에서 평가했다(그리퍼 그대로). 게이트는 벤치마크가 실제로 깨지는 순서를 따른다 -- 압축에 강한 liberoobject 를 가장 많이 압축하고, 2배에서 -0.160 으로 무너지는 liberospatial 은 거의 압축하지 않는다. 브랜치 jimin-dev-label-gated.…
Open weights
other
2.8B parameters
