Technical Directions
Robotics coverage relating to vla.
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LeRobot has converted the Hy-Embodied-0.5-VLA-RoboTwin model, achieving full forward parity with the original policy. The model supports 16D dual-arm pose/gripper actions and passes all validation checks, though it cannot handle more complex action spaces.
LeRobot v0.6.0 introduces world model policies that learn to imagine the future, new VLAs, and a reward models API. It also includes six new simulation benchmarks, improved dataset handling with depth support and language annotations, and deployment tools like lerobot-rollout.
NVIDIA has released SOMA-X v0.3.0, a unified framework for parametric human body models that decouples identity representation from pose parameterization. The release supports six identity backends including SOMA-shape, SMPL, and SMPL-X, and introduces native hand layers with interoperability for user-supplied MANO models. Designed for commercial use under the Apache 2.0 license, the model enables real-time simulation pipelines on NVIDIA GPUs with throughput exceeding 7,000 meshes per second on an A100. The core pipeline relies on closed-form analytical modules rather than learned neural networks, though an optional shallow MLP can be enabled for surface correction.
This tutorial explores the challenges of deploying VLA models on embedded robotic systems, including dataset recording best practices, fine-tuning techniques for ACT and SmolVLA, and real-time performance optimization using the NXP i.MX 95 SoC. It emphasizes asynchronous inference and hardware-aware scheduling to improve control and reduce latency.
LeRobot introduces a Vision-Language-Action (VLA) policy called PI0Fast, which predicts continuous robot actions via autoregressive next-token prediction over FAST action tokens. The repository includes training and inference code, along with instructions for evaluation in simulation and real-world settings.
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.
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