arXiv:2606.31144v1 Announce Type: cross Abstract: This paper presents an integrated system for the CMU Vision-Language-Action (VLA) Challenge, designed to enable an autonomous agent to perform complex tasks based on natural language instructions. Our framework employs a modular architecture that orchestrates environment mapping, question processing, and navigation. The system operates in two parallel streams: a perception pipeline that constructs a semantic voxel map from real-time camera feeds using OwlViT embeddings, and a language pipeline that classifies user commands with a Vision-Languag
The paper leverages recent advancements in vision-language models and robotics hardware, making such integrated systems increasingly viable for complex real-world tasks.
This work demonstrates a concrete step towards general-purpose robotic agents capable of understanding and executing complex instructions in unstructured environments, impacting automation and labor.
Previously siloed capabilities in perception, language understanding, and robotic action are now being integrated into cohesive, modular frameworks, accelerating the deployment of versatile robots.
- · Robotics companies
- · Logistics and manufacturing
- · AI software developers
- · Smart home technology providers
- · Tasks requiring repetitive manual labor
- · Narrowly specialized robotics firms
- · Companies slow to adopt automation
Further development of integrated vision-language-action models for embodied AI.
Increased demand for robust, adaptive robotic platforms in service and industrial sectors.
Potential for early applications of household robotic assistants capable of complex task execution.
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Read at arXiv cs.AI