[2603.03380] LiteVLA-Edge: Quantized On-Device Multimodal Control for Embedded Robotics

Computer Science > Robotics arXiv:2603.03380 (cs) [Submitted on 3 Mar 2026] Title:LiteVLA-Edge: Quantized On-Device Multimodal Control for Embedded Robotics Authors:Justin Williams, Kishor Datta Gupta, Roy George, Mrinmoy Sarkar View a PDF of the paper titled LiteVLA-Edge: Quantized On-Device Multimodal Control for Embedded Robotics, by Justin Williams and 3 other authors View PDF HTML (experimental) Abstract:Vision-Language-Action (VLA) models provide a unified framework for perception, language conditioning, and action generation, but many existing systems remain difficult to deploy in embedded robotic settings because of their computational requirements and inference latency. In this paper, we present LiteVLA-Edge, a deployment-oriented VLA pipeline for fully on-device inference on Jetson Orin-class hardware. Our approach combines supervised image-to-action fine-tuning in FP32 with post-training 4-bit GGUF quantization and GPU-accelerated inference through the \texttt{this http URL} runtime. Under our deployment configuration, LiteVLA-Edge achieves a mean end-to-end latency of 150.5\,ms (approximately 6.6\,Hz) while operating entirely offline within a ROS~2-integrated perception--reasoning--action pipeline. Rather than introducing a new policy objective, our contribution is a practical systems path for executing compact multimodal control models locally on embedded hardware while preserving modular interfaces between perception, reasoning, and actuation. These results e...