Technical Directions
Robotics coverage relating to robot policies.
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Dexterity introduces 'Transactable World Models' as a foundational component for Physical AI, emphasizing interpretability, physics integration, and real-time consistency. The approach treats world models as operators rather than data stores, enabling multi-agent coordination and robust manipulation in complex environments. This contrasts with traditional world models that focus on data compression and replay, lacking causal reasoning and physical grounding.
This hands-on tutorial walks through the process of collecting data, training policies, and deploying autonomous medical robotics workflows on real hardware using NVIDIA Isaac for Healthcare. It introduces the SO-ARM starter workflow, which enables developers to build and validate surgical assistant robots from simulation to deployment.
Hugging Face announces LeRobot v0.4.0, a major upgrade for open-source robotics with Dataset v3.0, new VLA models like PI0.5 and GR00T N1.5, and a plugin system for hardware integration. The release also adds support for LIBERO and Meta-World simulations, multi-GPU training, and a new Hugging Face Robot Learning Course.
Dexterity's Mech industrial superhumanoid has been approved for onsite operational validation at Sagawa Express's X Frontier® relay center in Tokyo, Japan. This marks the start of Mech's use at Sagawa, with plans to scale the technology to additional relay centers. The deployment aims to address labor shortages and regulatory challenges in Japan's logistics industry.
LeRobot has released a Vision-Language-Action (VLA) model, π₀.₅, trained on robot demonstrations and multimodal data for long-horizon tasks. The model supports flow-matching for action prediction and is compatible with the original reference code. It is intended for fine-tuning on specific use cases and includes both inference and training examples.
LeRobot has released π₀.₅, a Vision-Language-Action (VLA) model with open-world generalization capabilities, trained on robot demonstrations and multimodal data. The model supports flow-matching for action prediction and is intended as a base model for fine-tuning. Code and documentation are provided for inference and training.
LeRobot introduces π₀, a Vision-Language-Action (VLA) foundation model that jointly reasons over vision, language, and actions to control robots. The model is designed as a base architecture for enabling open-world generalization and includes training and inference instructions for deployment.
LeRobot has released the π₀.₅ (Pi05) model, a Vision-Language-Action (VLA) model with open-world generalization capabilities. It is trained on robot demonstrations and multimodal data to execute long-horizon tasks in real-world environments. The model supports flow-matching for action prediction and is intended as a base for fine-tuning on specific applications. The implementation is compatible with the original reference code and includes instructions for inference and training.
LeRobot releases the π₀ model, a Vision-Language-Action (VLA) foundation model that jointly reasons over vision, language, and actions to control robots. It serves as the base architecture for π₀.₅'s open-world generalization. The model supports training, inference, and real-world evaluation with detailed code examples and setup instructions.
NVIDIA has released the GR00T N1.5 model, a cross-embodiment foundation model for generalized humanoid robot reasoning and skills. The model can be fine-tuned using teleoperation data from a SO-101 arm, with a detailed tutorial provided for developers. The release includes instructions for dataset preparation, fine-tuning, evaluation, and deployment.
Hugging Face introduces SmolVLA, a 450M parameter open-source Vision-Language-Action model for robotics, trained on community-shared datasets. It outperforms larger models in simulation and real-world tasks, supports asynchronous inference for faster response, and is designed for deployment on consumer hardware.
SmolVLA is a compact, efficient Vision-Language-Action (VLA) model designed for affordable robotics, trainable on a single GPU and deployable on consumer hardware. It matches the performance of larger VLAs through community-driven data, with a reference implementation and training instructions provided.
NASA showcased its robotic technologies and future space exploration vision at the 2025 FIRST Robotics World Championship, engaging over 55,000 students and 75,000 parents and mentors. The event featured interactive exhibits, including lunar rovers, robotic arms, and the Mars Perseverance Rover, with NASA centers highlighting innovations like automated assembly systems and surface manipulation technologies.
Sanctuary AI has demonstrated industry-leading sim-to-real transfer of dexterous manipulation policies using their high-degree-of-freedom hydraulic hands. The company leverages NVIDIA Isaac Lab, an open-source framework for robot learning, to simulate dexterity-focused training environments. This approach enables efficient training of autonomous robots, with the ability to train thousands of hands simultaneously and accelerate the learning process through online reinforcement learning.
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