RAPID: Redundancy-Aware and Compatibility-Optimal Edge-Cloud Partitioned Inference for Diverse VLA Models
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866917334577315840 |
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| author | Zheng, Zihao Tian, Sicheng Cao, Hangyu Li, Chenyue Chen, Jiayu Li, Maoliang Sun, Xinhao Zou, Hailong Luo, Guojie Chen, Xiang |
| author_facet | Zheng, Zihao Tian, Sicheng Cao, Hangyu Li, Chenyue Chen, Jiayu Li, Maoliang Sun, Xinhao Zou, Hailong Luo, Guojie Chen, Xiang |
| contents | Vision Language Action (VLA) models are mainstream in embodied intelligence but face high inference costs. Edge-Cloud Collaborative (ECC) inference offers an effective fix by easing edge-device computing pressure to meet real-time needs. However, existing ECC frameworks are suboptimal for VLA models due to two challenges: (1) Mainstream environment-oriented edge-cloud partitioning methods are susceptible to interference from visual noise; (2) Existing edge-cloud partitioning methods overlook the step-wise redundancy unique to embodied tasks, thereby disrupting the physical continuity of motion. To address these issues, we propose a novel ECC inference framework, termed RAPID. Specifically, we developed an implementation tailored to the proposed framework. Experiments demonstrate this achieves a speedup of up to 1.73x with only 5%~7% overhead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_07949 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RAPID: Redundancy-Aware and Compatibility-Optimal Edge-Cloud Partitioned Inference for Diverse VLA Models Zheng, Zihao Tian, Sicheng Cao, Hangyu Li, Chenyue Chen, Jiayu Li, Maoliang Sun, Xinhao Zou, Hailong Luo, Guojie Chen, Xiang Distributed, Parallel, and Cluster Computing Robotics Vision Language Action (VLA) models are mainstream in embodied intelligence but face high inference costs. Edge-Cloud Collaborative (ECC) inference offers an effective fix by easing edge-device computing pressure to meet real-time needs. However, existing ECC frameworks are suboptimal for VLA models due to two challenges: (1) Mainstream environment-oriented edge-cloud partitioning methods are susceptible to interference from visual noise; (2) Existing edge-cloud partitioning methods overlook the step-wise redundancy unique to embodied tasks, thereby disrupting the physical continuity of motion. To address these issues, we propose a novel ECC inference framework, termed RAPID. Specifically, we developed an implementation tailored to the proposed framework. Experiments demonstrate this achieves a speedup of up to 1.73x with only 5%~7% overhead. |
| title | RAPID: Redundancy-Aware and Compatibility-Optimal Edge-Cloud Partitioned Inference for Diverse VLA Models |
| topic | Distributed, Parallel, and Cluster Computing Robotics |
| url | https://arxiv.org/abs/2603.07949 |