华为&华科大新作TurboVLA：实时视觉语言动作模型

TL;DR - TurboVLA, from Huazhong University of Science and Technology and Huawei, is a vision-language-action model that removes the LLM from the action-prediction loop, letting vision and language features interact directly (V+L→A) to run at ~32Hz on a single RTX 4090 with under 1GB VRAM. It matters because it shows high-frequency, language-conditioned robot control can be done on local edge hardware without a multi-billion-parameter language backbone.

• Architecture: DINOv3 encodes multi-camera vision, a lightweight text encoder (e.g. BERT) encodes instructions, bidirectional cross-modal interaction builds task-relevant representations, and a light decoder emits a full continuous action chunk in one forward pass — no action tokenization or autoregressive decoding.
• LIBERO (40 language-conditioned tasks): 97.7% average success with 0.2B params, 0.9GB VRAM, 31.2ms policy latency, versus π0.5 at 96.9% with 3.4B params and 93.6ms — roughly 6% of the parameters at ~1/3 the latency.
• Scaling out: on RoboTwin 2.0 (50 bimanual tasks, clean-data joint training) a single 0.4B multi-task model hits 60.2% vs π0.5's 57.0% and StarVLA-α's 50.3% at 43.4ms; on a real AgileX Piper arm it reaches 92.5%/80.0%/90.0%/87.5% across four tasks, beating π0.5 under matched protocols.
• Ablations: dropping language collapses success to 70.8%; Task-ID substitution recovers only 95.4%; naive feature concatenation gives 95.2% vs 97.7% for bidirectional interaction — so semantic instructions and explicit cross-modal fusion still matter. The authors position LLMs for high-level planning with TurboVLA as the low-latency execution layer.