arXiv:2607.02222v1 Announce Type: cross Abstract: Vision-Language Navigation has increasingly emphasized high-level instruction reasoning, memory, global map construction, and instruction decomposition, while the low-level action representation remains comparatively underexplored. We propose CoFL-S, a low-level vision-language-action framework that predicts a language-conditioned flow field over the robot's local visible sector and generates continuous trajectories by rolling out the predicted field. To train this low-level representation, we convert each VLN-CE episode, originally a whole-epi
The continuous advancements in Vision-Language Models (VLMs) are pushing towards more nuanced and actionable robotic control, making the exploration of low-level action representations a critical next step.
This work addresses a core challenge in robotics by enabling more precise and context-aware robot navigation, bridging the gap between high-level language instructions and low-level physical actions.
The proposed CoFL-S framework shifts the paradigm of language-conditioned navigation by focusing on direct, continuous trajectory generation from local flow fields, potentially leading to more robust and adaptable robotic systems.
- · Robotics companies
- · AI agents developers
- · Logistics and automation sector
- · Defense and reconnaissance developers
- · Robotics platforms with limited low-level control
- · Developers reliant on discrete action spaces
- · Companies with less sophisticated VLM integration
More sophisticated and autonomous robots capable of executing detailed language commands in complex environments.
Accelerated deployment of robotic systems in challenging real-world scenarios, from warehouses to last-mile delivery and hazardous exploration.
Increased demand for advanced sensors and processing units capable of real-time, high-fidelity spatial reasoning for ubiquitous robotic integration.
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Read at arXiv cs.AI