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RoboECC Multi-Factor-Aware Edge-Cloud Collaborative Deployment for VLA Models

Zihao Zheng1 Hangyu Cao2 Jiayu Chen1 Sicheng Tian3 Chenyue Li2 Maoliang Li1 Xinhao Sun4 Guojie Luo1 Xiang Chen1,†

1Peking University 2South China University of Technology 3Beijing Normal University 4Peking University

Paper | PDF | IJCNN 2026

Figure 1. VLA edge inference, the two deployment challenges, and the RoboECC framework.

RoboECC is an edge-cloud collaborative deployment framework for Vision-Language-Action (VLA) models. It jointly considers model structure, cloud and edge hardware, cloud load, activation size, and network fluctuation to find an efficient segmentation point and adjust it online.

The paper reports 3.16×–3.28× speedup on Orin+A100 and 2.10×–2.23× speedup on Thor+A100 over edge-only deployment, with 2.55%–2.62% adjustment overhead.

Highlights

Model-hardware co-aware segmentation. RoboECC models heterogeneous VLA components as a sequential layer chain and estimates GPU latency with a roofline model, max(compute time, memory time). It then searches all feasible boundaries under a configurable cloud-load budget.
Network-aware deployment adjustment. A lightweight LSTM predicts the next bandwidth sample. A two-threshold policy moves the boundary toward a larger activation when bandwidth rises and a smaller activation when bandwidth falls.
Parameter-sharing pool. The block surrounding the optimal boundary stays resident on both devices. Cut changes therefore update execution metadata instead of transferring model weights.
Fine-grained LLaMA planning. A decoder block can be divided at internal RMSNorm, attention, and MLP boundaries, with residual tensors included in transfer-size estimates.

What’s Included

Component	Implementation
Model, hardware, and network profiles	Validated framework-independent schemas
Offline deployment search	Roofline latency, transfer cost, cloud budget, deterministic cut selection
Online adjustment	LSTM predictor, threshold policy, parameter-sharing pool, atomic local commit
Model adapters	OpenVLA reconfiguration bridge and constrained CogACT flag mapping
Fine-grained planning	Five-stage LLaMA block decomposition and boundary payload accounting
Developer tools	CLI, illustrative scenarios, technical documentation, and CPU regression tests

[!NOTE] This repository contains RoboECC’s planning and runtime-control layer. Full OpenVLA, CogACT, LIBERO, and SimplerEnv repositories are external dependencies and are not vendored here.

Installation

The core planner requires Python 3.10 or later and has no third-party runtime dependencies:

python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -e .

Install PyTorch support for bandwidth prediction, plus development tools, when needed:

python -m pip install -e ".[lstm,dev]"

Quick Start

Search every cloud-prefix/edge-suffix boundary in the illustrative scenario:

roboecc-search examples/toy_scenario.json

The starred row is the selected deployment. Values in examples/toy_scenario.json demonstrate the input schema; they are not paper measurements.

Python API example

from roboecc import (
    HardwareProfile,
    LayerProfile,
    ModelProfile,
    NetworkProfile,
    search_optimal_split,
)

model = ModelProfile(
    name="toy-vla",
    layers=(
        LayerProfile("encoder", 3e11, 2e9, 1e9, 200_000),
        LayerProfile("backbone", 8e11, 5e9, 4e9, 26_112),
        LayerProfile("action_head", 1e11, 1e9, 5e8, 1_024),
    ),
)

optimal = search_optimal_split(
    model,
    cloud=HardwareProfile("cloud", 312e12, 1.6e12, 0.35),
    edge=HardwareProfile("edge", 40e12, 200e9, 0.30),
    network=NetworkProfile(10_000_000, fixed_latency_ms=1.0),
    max_cloud_load=5e9,
)
print(optimal.cut, optimal.total_ms)

The normalized boundary convention is:

cut = 0                 cut = k                    cut = N
[ all edge ]            [ cloud | edge ]           [ all cloud ]

cut is the number of stages in the cloud prefix. Internal cuts transmit the preceding stage’s output; endpoint deployments do not charge an intermediate-activation transfer.

Network-Aware Adjustment

effective bandwidth trace
          │
          ▼
  bandwidth LSTM ──► predicted Δ bandwidth
                              │
                    threshold policy
                              │
                              ▼
                next-action boundary update

The default predictor is a one-layer, many-to-one LSTM with a 20-sample input window and hidden size 32. Training uses chronological validation, training-prefix-only standardization, AdamW, normalized MSE, gradient clipping, and early stopping.

Train from a time-ordered bytes-per-second trace:

roboecc-train-bandwidth \
  --csv traces/a100_thor_bandwidth.csv \
  --column bandwidth_bytes_per_second \
  --output checkpoints/bandwidth_lstm.pt

Run a pipeline smoke test without a real trace:

roboecc-train-bandwidth \
  --synthetic-samples 300 \
  --epochs 10 \
  --output /tmp/roboecc_bandwidth_smoke.pt

Synthetic data validates the code path only and must not be reported as an experiment. The trace format and training protocol are documented in docs/BANDWIDTH_LSTM.md.

LLaMA Block-Internal Splits

Each LLaMA decoder block is expanded into five sequential planning stages:

input RMSNorm → self-attention → post-attention RMSNorm → MLP expand → MLP down

This exposes four strict internal boundaries. block_cuts() additionally includes both block boundaries so that the complete block can be kept in the parameter-sharing pool:

from roboecc import ExpandedLlamaModel, LlamaBlockProfile, ParameterSharingPool

block = LlamaBlockProfile(
    block_index=13,
    hidden_size=4096,
    intermediate_size=11008,
    sequence_length=17,
    num_attention_heads=32,
    num_key_value_heads=32,
)
expanded = ExpandedLlamaModel.build("OpenVLA-LLM", (block,))

pool = ParameterSharingPool(
    expanded.profile,
    allowed_cuts=expanded.block_cuts(13),
    initial_cut=expanded.internal_cuts(13)[1],
)

This is a planning and cost-model abstraction. It does not replace a Transformers LlamaDecoderLayer.forward() or execute block-internal tensors across two GPUs. See docs/LLAMA_INTERNAL_SPLIT.md for payload definitions.

Backend Integration

Backend	Status	Notes
OpenVLA	Adapter implemented	Block-level `reconfigure` RPC; external split backend required
CogACT	Adapter implemented with constraints	Requires cloud-prefix DiT order and preloaded sharing-pool parameters
LLaMA internal stages	Planning only	No operator-level cross-device forward
A100↔Thor/Orin	Validation pending	No released real trace, official LSTM checkpoint, or dual-device regression

The legacy standalone CogACT client executes a partial DiT in edge-prefix/cloud-suffix order and is not compatible with this adapter. Integration details are in docs/DYNAMIC_DEPLOYMENT.md; release and security limitations are tracked in docs/KNOWN_GAPS.md.

Testing

PYTHONPATH=src python -m unittest discover -s tests -v

The current suite contains 34 tests. Two LSTM tests are skipped when PyTorch is not installed; all tests run on CPU when the optional dependency is available.

Repository Layout

RoboECC/
├── assets/             # Paper overview figure and provenance
├── src/roboecc/        # Planner, predictor, pool, controller, and adapters
├── tests/              # CPU unit and regression tests
├── examples/           # Illustrative scenario and LLaMA split example
└── docs/               # Training, integration, and limitation notes

Citation

If you find RoboECC useful in your research, please cite:

@article{zheng2026roboecc,
  title={RoboECC: Multi-Factor-Aware Edge-Cloud Collaborative Deployment for VLA Models},
  author={Zheng, Zihao and Cao, Hangyu and Chen, Jiayu and Tian, Sicheng and Li, Chenyue and Li, Maoliang and Sun, Xinhao and Luo, Guojie and Chen, Xiang},
  journal={arXiv preprint arXiv:2603.20711},
  year={2026}
}

Acknowledgements

RoboECC evaluates edge-cloud deployment with OpenVLA, CogACT, LIBERO, and SimplerEnv. We thank their authors and contributors for releasing their work.

Figure 1 is reproduced from the RoboECC paper under CC BY 4.0.

RoboECC

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