EdgeVLN: Runtime-Aware Deployment Ready Quantized Vision Language Navigation Model

arXiv:2609.35570 · cs.RO, cs.CV · Submitted 2026-09-28 · Read on arXiv

cs.RO, cs.CV

Submitted: 2026-09-28

Updated: 2026-09-28

Code: https://github.com/ggml-org/llama.cpp

Terminology

• Quantization — 28× in this paper · explained in 12 episodes
• Verifier — 15× in this paper · explained in 2 episodes
• Success Rate — 13× in this paper
• Pruning — 6× in this paper · explained in 4 episodes
• Verification — 6× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Transformer — 4× in this paper · explained in 2 episodes
• Edge Deployment — 3× in this paper · explained in 2 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Oracle Success Rate — 3× in this paper
• Edge AI — 2× in this paper · explained in 2 episodes
• Edge Devices — 2× in this paper · explained in 2 episodes
• Frozen model — 2× in this paper · explained in 3 episodes
• KV cache — 2× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Energy Efficiency — 1× in this paper · explained in 3 episodes
• Energy Efficient High Performance Computing — 1× in this paper
• Hysteresis — 1× in this paper · explained in 3 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• VLA Models — 1× in this paper · explained in 3 episodes

Sources

• StreamVLN: Streaming Vision-and-Language Navigation via SlowFast Context Modeling
• LiteVLA-Edge: Quantized On-Device Multimodal Control for Embedded Robotics
• DyQ-VLA: Temporal-Dynamic-Aware Quantization for Embodied Vision-Language-Action Models
• Characterizing VLA Models: Identifying the Action Generation Bottleneck for Edge AI Architectures
• EdgeNav-QE: QLoRA Quantization and Dynamic Early Exit for LAM-based Navigation on Edge Devices
• Rethinking Small VLM Quantization: From Component-Wise Analysis to Hardware-Aware Edge Deployment

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