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NVlabs/ProtoMotions: GPU-accelerated framework for physically simulated humanoid motion learning
NVIDIA Labs released ProtoMotions, an open-source Python framework for GPU-accelerated simulation and reinforcement learning of physically simulated digital humans and humanoid robots. The project is trending on GitHub with 1,916 stars and 63 new stars today. It sits at the intersection of physics simulation, embodied AI, and robotics training infrastructure.
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NVIDIA PhysicsNeMo: open-source Physics-ML deep learning framework
NVIDIA has published PhysicsNeMo, an open-source Python framework for building, training, and fine-tuning deep learning models using Physics-ML methods. The repository has accumulated 2,933 stars on GitHub. Physics-informed ML is a growing area relevant to scientific computing and simulation workloads.
NVIDIA NeMo Gym: framework for evaluating and improving models and agents using environments
NVIDIA's NeMo team has published a Python library called NeMo Gym on GitHub, designed to evaluate and improve models and agents through environment-based interaction. The repository has 941 stars with minimal recent traction (+1 today). It appears to be an RL-style evaluation and training harness within the NeMo ecosystem.
NVIDIA's GTC 2025 Announcement for Physical AI Developers: New Open Models and Datasets
NVIDIA announced new open models and datasets for physical AI development at GTC 2025, covered via the Hugging Face blog. The release targets robotics and embodied AI developers with open-weights resources. This represents NVIDIA's continued push into the physical AI ecosystem alongside its hardware dominance.
VLK: Synthetic vision-language-kinematics supervision enables humanoid loco-manipulation from egocentric observations
Researchers introduce a pipeline that generates 48,000 paired vision-language-kinematics trajectories synthetically using 3D Gaussian Splatting to reconstruct indoor scenes, bypassing the need for expensive human-annotated robot data. A VLK policy trained on this data predicts whole-body kinematic trajectories from egocentric images and language instructions, which a whole-body tracker converts to physical actions. The approach is validated on a Unitree G1 humanoid performing navigation and object transport, demonstrating viable sim-to-real transfer for perception-based loco-manipulation.
Welcome NVIDIA Cosmos 3: The First Open Omni-model for Physical AI Reasoning and Action
NVIDIA has released Cosmos 3, described as the first open omni-model targeting physical AI reasoning and action. The model is hosted and announced via Hugging Face, positioning it as an open-weights offering for robotics and embodied AI applications. The announcement highlights multimodal capabilities oriented toward physical world understanding and agent-level action.
Fine-Tuning NVIDIA Cosmos Predict 2.5 with LoRA/DoRA for Robot Video Generation
This Hugging Face blog post details a workflow for fine-tuning NVIDIA's Cosmos Predict 2.5 world model using LoRA and DoRA parameter-efficient techniques for robot video generation tasks. The post covers practical implementation steps for adapting the foundation video model to robotics-specific domains. This represents a concrete application of world models to embodied AI, where synthetic video generation can support robot training data pipelines.
Humanoid-GPT: GPT-style Transformer trained on 2B-frame motion corpus for zero-shot humanoid control
Researchers introduce Humanoid-GPT, a causal Transformer pre-trained on a 2-billion-frame retargeted motion corpus that unifies major mocap datasets with large-scale in-house recordings for whole-body humanoid control. The model achieves zero-shot generalization to unseen motions and control tasks, overcoming the agility-generalization trade-off seen in prior MLP-based trackers. Scaling analyses demonstrate a new performance frontier for dynamic motion tracking without task-specific fine-tuning.
NVIDIA Llama Nemotron Nano VLM Released on Hugging Face Hub
NVIDIA has released the Llama Nemotron Nano VLM on Hugging Face Hub, a compact vision-language model built on the Llama architecture. The model is part of NVIDIA's Nemotron family targeting efficient multimodal inference. This release makes the model accessible to the broader research and developer community through Hugging Face's model hosting infrastructure.