NanoVLA：軽量汎用ロボットポリシーのための視覚言語理解を分離したルーティング
NanoVLA: Routing Decoupled Vision-Language Understanding for Nano-sized Generalist Robotic Policies
視覚と言語の融合を後段に分離し、長短アクションチャンキングと動的ルーティングを組み合わせることで、エッジデバイス上で最大52倍高速かつ98%少ないパラメータで動作する軽量VLAモデルを提案した。
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著者: Jiahong Chen, Jing Wang, Long Chen, Chuwei Cai, Jinghui Lu
分類: cs.RO
原文アブストラクト
Vision-language-action (VLA) models have significantly advanced robotic manipulation by integrating vision-language models (VLMs), and action decoders into a unified architecture. However, their deployment on resource-constrained edge devices, such as mobile robots or embedded systems (e.g., Jetson Orin Nano), remains challenging due to high computational demands, especially in real-world scenarios where power, latency, and computational resources are critical. To close this gap, we introduce Nano-scale Vision-Language Action (NanoVLA), a family of lightweight VLA architectures that achieve high performance with minimal resources. Our core innovations include: (1) vision-language decoupling that moves conventional early vision and language inputs fusion in VLM to late stage, achieving better performance while enabling caching and reduce inference overhead and latency; (2) long-short action chunking to ensure smooth, coherent multi-step planning without sacrificing real-time responsiveness; (3) dynamic routing that adaptively assigns lightweight or heavy backbones based on task complexity, further optimizing inference efficiency. Experimental results on several benchmarks, as well as real-world deployments, demonstrate that NanoVLA achieves up to 52x faster inference on edge devices compared to previous state-of-the-art VLA models, with 98% less parameters while maintaining or surpassing their task accuracy and generalization. Ablation studies confirm that our decoupling strategy preserves cross-task transferability, and the routing module enhances cost-performance trade-offs, enabling practical, high-precision robotic manipulation on resource-constrained hardware.
