arXiv:2607.05377v1 Announce Type: cross Abstract: While recent Vision-Language-Action (VLA) models show promise toward generalist manipulation policies, they struggle with long-horizon tasks due to their Markovian nature-relying solely on current observations. Hierarchical dual-system methods address this but suffer from a gap between high-level planning semantics and low-level execution kinematics. We introduce Cortex, a bidirectionally aligned embodied agent framework with a customized planning interface that conveys executable and tractable subtask plans from high-level VLM to low-level VLA
The continuous advancements in Vision-Language Models (VLMs) and Vision-Language-Action (VLA) models are driving the need for more robust frameworks to handle complex, long-horizon tasks in embodied AI.
This research addresses a critical gap in embodied AI by enabling more effective and reliable manipulation for complex tasks, moving closer to general-purpose robotic agents.
The ability of AI systems to perform long-horizon manipulation tasks in real-world environments is significantly enhanced, bridging the planning-execution gap in robotics.
- · Robotics companies
- · Logistics and manufacturing automation
- · AI agents developers
- · Research institutions in AI/robotics
- · Companies relying on static, single-task automation
- · Manual labor in repetitive manipulation tasks
Improved performance and broader application of robotic manipulators in unstructured environments.
Accelerated development of general-purpose robots capable of addressing a wider range of physical tasks autonomously.
Potential for new economic models based on highly adaptable and autonomous physical AI agents across industries.
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Read at arXiv cs.AI