A Modular Vision-Language-Action Robotics Framework for Indoor Environments
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-Language Model. The mapping is time-constrained; the system proceeds with a partial map if a 500-second exploration limit is reached. The classified query...
| Authors | Anindya Jana, Snehasis Banerjee, Arup Sadhu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 30 June 2026 |
| Last updated at source | 30 June 2026 |
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