# VLA-ULAP: Interleaving Cloud VLA Calls with Ultra-Lightweight Local Action Prediction at the Edge

arXiv: https://arxiv.org/abs/2609.18663 · HTML: https://mlsanity.com/paper/2609.18663/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 35 of its 47 references that are in the map.
- First posted on arXiv 16 Sep 2026.

## Builds on (most cited first, 30 of 35)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [π0.5: a Vision-Language-Action Model with Open-World Generalization](https://mlsanity.com/paper/2504.16054/) (2025)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning](https://mlsanity.com/paper/2306.03310/) (2023)
- [GR00T N1: An Open Foundation Model for Generalist Humanoid Robots](https://mlsanity.com/paper/2503.14734/) (2025)
- [CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks](https://mlsanity.com/paper/2112.03227/) (2021)
- [SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics](https://mlsanity.com/paper/2506.01844/) (2025)
- [X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model](https://mlsanity.com/paper/2510.10274/) (2025)
- [Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning](https://mlsanity.com/paper/2601.16163/) (2026)
- [VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model](https://mlsanity.com/paper/2602.10098/) (2026)
- [DeeR-VLA: Dynamic Inference of Multimodal Large Language Models for Efficient Robot Execution](https://mlsanity.com/paper/2411.02359/) (2024)
- [VLA-Cache: Efficient Vision-Language-Action Manipulation via Adaptive Token Caching](https://mlsanity.com/paper/2502.02175/) (2025)
- [HiRT: Enhancing Robotic Control with Hierarchical Robot Transformers](https://mlsanity.com/paper/2410.05273/) (2024)
- [Theia: Distilling Diverse Vision Foundation Models for Robot Learning](https://mlsanity.com/paper/2407.20179/) (2024)
- [SP-VLA: A Joint Model Scheduling and Token Pruning Approach for VLA Model Acceleration](https://mlsanity.com/paper/2506.12723/) (2025)
- [Think Twice, Act Once: Token-Aware Compression and Action Reuse for Efficient Inference in Vision-Language-Action Models](https://mlsanity.com/paper/2505.21200/) (2025)
- [QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Language-Action Models](https://mlsanity.com/paper/2602.20309/) (2026)
- [Adaptive Action Chunking at Inference-time for Vision-Language-Action Models](https://mlsanity.com/paper/2604.04161/) (2026)
- [Bridging the Semantic-Action Gap in Visual Token Pruning for Efficient VLA Inference](https://mlsanity.com/paper/2511.16449/) (2025)
- [VLA-Corrector: Lightweight Detect-and-Correct Inference for Adaptive Action Horizon](https://mlsanity.com/paper/2607.01804/) (2026)
- [DaDu-Corki: Algorithm-Architecture Co-Design for Embodied AI-powered Robotic Manipulation](https://mlsanity.com/paper/2407.04292/) (2024)
- [LIBERO-Safety: A Comprehensive Benchmark for Physical and Semantic Safety in Vision-Language-Action Models](https://mlsanity.com/paper/2606.23686/) (2026)
- [Realtime-VLA FLASH: Speculative Inference Framework for Diffusion-based VLAs](https://mlsanity.com/paper/2605.13778/) (2026)
- [Denoising Tells When to Replan: Denoising-Variance Adaptive Chunking for Flow-Based Robot Policies](https://mlsanity.com/paper/2606.03847/) (2026)
- [RT-Cache: Training-Free Retrieval for Real-Time Manipulation](https://mlsanity.com/paper/2505.09040/) (2025)
- [Latent Bridge: Feature Delta Prediction for Efficient Dual-System Vision-Language-Action Model Inference](https://mlsanity.com/paper/2605.02739/) (2026)
- [Rethink Before You Execute: Adaptive Execution for World Action Models](https://mlsanity.com/paper/2608.09492/) (2026)
- [Jetson-PI: Towards Onboard Real-Time Robot Control via Foresight-Aligned Asynchronous Inference](https://mlsanity.com/paper/2607.12659/) (2026)

## Abstract (arXiv)

Billion-parameter vision-language-action (VLA) policies run either onboard, consuming substantial power, or on remote servers, adding communication latency. To address these drawbacks and better balance latency and onboard energy consumption, we propose VLA-ULAP. It partitions inference across decision times, interleaving remote VLA calls with predictions from an Ultra-Lightweight Local Action Predictor (ULAP). A single ULAP has ~7.4M parameters including the frozen vision encoder. It combines current views, proprioception, and executed action history to predict chunks in one pass. Trained independently, it requires no VLA hidden states, online verification, or server round trips. On Jetson Orin Nano, ULAP takes 20.7 ms and 0.122 J of idle-subtracted energy per inference, versus 289.3 ms and 40.46 J for GR00T on RTX A6000. Across four simulated base-policy/benchmark pairs, VLA-ULAP removes 45.3-77.3% of VLA calls while retaining 95.0-98.5% of baseline success rates at selected operating points. On VLA-JEPA, VLA-ULAP also surpasses local acceleration alternatives, using an estimated 49.4% less inference time and 51.5% less GPU energy per successful episode than ACT, and 77.5% less time and 80.7% less energy than SP-VLA at higher success rates. In physical SO-101 trials, it similarly removes 70.3-71.9% of VLA calls without observed success-rate loss at seen or held-out placements. Measured device costs imply 64.6-66.4% less inference time and 70.1-71.7% less idle-subtracted energy per successful episode at these call counts. Beyond these savings, faster responses help VLA-ULAP exceed π0.5's success rate by 11.0 and 15.5 percentage points (pp) on two tasks in latency-aware LIBERO-Safety simulation while approximately halving VLA calls.