Manipulation Facing Threats: Evaluating Physical Vulnerabilities in End-to-End Vision Language Action Models
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911249505189888 |
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| author | Cheng, Hao Xiao, Erjia Wang, Yichi Yu, Chengyuan Sun, Mengshu Zhang, Qiang Cao, Jiahang Guo, Yijie Liu, Ning Xu, Kaidi Zhang, Jize Shen, Chao Torr, Philip Gu, Jindong Xu, Renjing |
| author_facet | Cheng, Hao Xiao, Erjia Wang, Yichi Yu, Chengyuan Sun, Mengshu Zhang, Qiang Cao, Jiahang Guo, Yijie Liu, Ning Xu, Kaidi Zhang, Jize Shen, Chao Torr, Philip Gu, Jindong Xu, Renjing |
| contents | Recently, driven by advancements in Multimodal Large Language Models (MLLMs), Vision Language Action Models (VLAMs) are being proposed to achieve better performance in open-vocabulary scenarios for robotic manipulation tasks. Since manipulation tasks involve direct interaction with the physical world, ensuring robustness and safety during the execution of this task is always a very critical issue. In this paper, by synthesizing current safety research on MLLMs and the specific application scenarios of the manipulation task in the physical world, we comprehensively evaluate VLAMs in the face of potential physical threats. Specifically, we propose the Physical Vulnerability Evaluating Pipeline (PVEP) that can incorporate as many visual modal physical threats as possible for evaluating the physical robustness of VLAMs. The physical threats in PVEP specifically include Out-of-Distribution, Typography-based Visual Prompt, and Adversarial Patch Attacks. By comparing the performance fluctuations of VLAMs before and after being attacked, we provide generalizable \textbf{\textit{Analyses}} of how VLAMs respond to different physical threats. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13174 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Manipulation Facing Threats: Evaluating Physical Vulnerabilities in End-to-End Vision Language Action Models Cheng, Hao Xiao, Erjia Wang, Yichi Yu, Chengyuan Sun, Mengshu Zhang, Qiang Cao, Jiahang Guo, Yijie Liu, Ning Xu, Kaidi Zhang, Jize Shen, Chao Torr, Philip Gu, Jindong Xu, Renjing Computer Vision and Pattern Recognition Recently, driven by advancements in Multimodal Large Language Models (MLLMs), Vision Language Action Models (VLAMs) are being proposed to achieve better performance in open-vocabulary scenarios for robotic manipulation tasks. Since manipulation tasks involve direct interaction with the physical world, ensuring robustness and safety during the execution of this task is always a very critical issue. In this paper, by synthesizing current safety research on MLLMs and the specific application scenarios of the manipulation task in the physical world, we comprehensively evaluate VLAMs in the face of potential physical threats. Specifically, we propose the Physical Vulnerability Evaluating Pipeline (PVEP) that can incorporate as many visual modal physical threats as possible for evaluating the physical robustness of VLAMs. The physical threats in PVEP specifically include Out-of-Distribution, Typography-based Visual Prompt, and Adversarial Patch Attacks. By comparing the performance fluctuations of VLAMs before and after being attacked, we provide generalizable \textbf{\textit{Analyses}} of how VLAMs respond to different physical threats. |
| title | Manipulation Facing Threats: Evaluating Physical Vulnerabilities in End-to-End Vision Language Action Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.13174 |