Researchers released FluxVLA Engine, an open, configuration-driven platform that turns heterogeneous embodied-policy components into a reproducible data-to-deployment workflow. It standardizes interfaces for datasets, VLA and world models, action heads, training, simulation evaluation, and inference rather than introducing a new policy model. Code is available on GitHub, targeting the engineering gaps between embodied-learning research and dependable robot deployment.
A new arXiv survey comprehensively analyzes Generative Physical Artificial Intelligence (GPAI), where large foundation models are integrated with physical robot embodiments. It proposes a five-way taxonomy covering Robot Foundation Models, Vision-Language Action models, Large Behavior Models, Diffusion Policy Models, and World Foundation Models, and examines applications from autonomous vehicles to humanoids. For practitioners it maps how the approaches complement each other and flags open problems in data efficiency, sim-to-real transfer, edge deployment, and safety.
A new arXiv survey comprehensively analyzes Generative Physical Artificial Intelligence (GPAI), where large foundation models are integrated with physical robot embodiments. It proposes a five-way taxonomy covering Robot Foundation Models, Vision-Language Action models, Large Behavior Models, Diffusion Policy Models, and World Foundation Models, and examines applications from autonomous vehicles to humanoids. For practitioners it maps how the approaches complement each other and flags open problems in data efficiency, sim-to-real transfer, edge deployment, and safety.
A new arXiv paper introduces EmbodiedSkills, a unified framework for orchestrating, training, and deploying vision-language-action (VLA) agents on long-horizon robot tasks. It treats each skill decision as an execution proposal that the runtime validates and verifies, addressing gaps in current VLA models that map observations and instructions directly to actions. This matters to robotics practitioners building agents that must coordinate perception, planning, execution, verification, and recovery.
A new arXiv paper introduces EmbodiedSkills, a unified framework for orchestrating, training, and deploying vision-language-action (VLA) agents on long-horizon robot tasks. It treats each skill decision as an execution proposal that the runtime validates and verifies, addressing gaps in current VLA models that map observations and instructions directly to actions. This matters to robotics practitioners building agents that must coordinate perception, planning, execution, verification, and recovery.
A new arXiv paper introduces ReflexBench, a benchmark for reaction-critical manipulation with six dynamic tasks, and proposes ReflexVLA, a model designed for fast and predictive vision-language-action control. The work addresses the gap in evaluating VLA models on dynamic interaction scenarios rather than static tasks.
A new arXiv paper introduces ReflexBench, a benchmark for reaction-critical manipulation with six dynamic tasks, and proposes ReflexVLA, a model designed for fast and predictive vision-language-action control. The work addresses the gap in evaluating VLA models on dynamic interaction scenarios rather than static tasks.
A new arXiv paper introduces Show-Harness, an embodied harness that lets vision-language models control robots through a compact semantic interface linking intent to action. The design targets the gap between VLMs' broad world knowledge and fine-grained robot control, relevant to practitioners building VLM-driven robotics stacks.
A new arXiv paper introduces Show-Harness, an embodied harness that lets vision-language models control robots through a compact semantic interface linking intent to action. The design targets the gap between VLMs' broad world knowledge and fine-grained robot control, relevant to practitioners building VLM-driven robotics stacks.
A new paper proposes Agentic Robot with Tool-use (ART), a tool-injection framework that tunes any Vision-Language-Action (VLA) model to use off-the-shelf tool modules for low-level vision, high-level affordance, and embodiment enhancement. ART reduces the complexity of the action solution space, improving generalizability and reducing data dependency compared to vanilla VLA models.
A new paper proposes Agentic Robot with Tool-use (ART), a tool-injection framework that tunes any Vision-Language-Action (VLA) model to use off-the-shelf tool modules for low-level vision, high-level affordance, and embodiment enhancement. ART reduces the complexity of the action solution space, improving generalizability and reducing data dependency compared to vanilla VLA models.