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The process of adapting vision-language-action models to specific robotic hardware or tasks.
Distinct from Model Fine-Tuning: Specifically targets the multimodal action-generation models rather than generic models.
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OpenPi is a vision-language-action robot control framework designed to generate physical control actions for robotic systems. It functions as a distributed robot model trainer, a model format converter, and a robot action streaming server. The framework provides tools for transforming model checkpoints between different framework formats to ensure interoperability across various development environments. It also includes a server that uses websocket connections to stream model-generated control actions from remote inference servers to physical robot hardware in real-time. The system supports
Adapts base vision-language-action model checkpoints to specific robot platforms using task-specific datasets.
RLinf is a distributed reinforcement learning orchestrator and embodied AI training framework. It provides the infrastructure to train vision-language-action models and robotic policies using a combination of reinforcement learning and supervised fine-tuning. The system is designed for scaling workloads across GPU clusters, managing the placement of actors, rollout workers, and environment components. It features a specialized robotics data collection pipeline for gathering teleoperated demonstrations and simulation trajectories into standardized replay buffers, alongside a hardware interface
Provides a toolkit for adapting vision-language-action models to specific robotic hardware or tasks using RL and diffusion.