UniLCD: Unified Local-Cloud Decision-Making via Reinforcement Learning
<|reference_start|>UniLCD: Unified Local-Cloud Decision-Making via Reinforcement Learning: Embodied vision-based real-world systems, such as mobile robots, require a careful balance between energy consumption, compute latency, and safety constraints to optimize operation across dynamic tasks and contexts. As local computation tends to be restricted, offloading the computation, ie, to a remote server, can save local resources while providing access to high-quality predictions from powerful and large models. However, the resulting communication and latency overhead has led to limited usability of cloud models in dynamic, safety-critical, real-time settings. To effectively address this trade-off, we introduce UniLCD, a novel hybrid inference framework for enabling flexible local-cloud collaboration. By efficiently optimizing a flexible routing module via reinforcement learning and a suitable multi-task objective, UniLCD is specifically designed to support the multiple constraints of safety-critical end-to-end mobile systems. We validate the proposed approach using a challenging, crowded navigation task requiring frequent and timely switching between local and cloud operations. UniLCD demonstrates improved overall performance and efficiency, by over 35% compared to state-of-the-art baselines based on various split computing and early exit strategies.<|reference_end|>
@article{sengupta2024unilcd:,
    title={UniLCD: Unified Local-Cloud Decision-Making via Reinforcement Learning},
    author={Kathakoli Sengupta, Zhongkai Shagguan, Sandesh Bharadwaj, Sanjay
  Arora, Eshed Ohn-Bar, Renato Mancuso},
    journal={arXiv preprint arXiv:2409.11403},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.11403},
    primaryClass={cs.RO}
}