Built on

C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,”
Original
2013
Earlier work this paper cites.
A. Majumdar, A. Shrivastava, S. Lee, P. Anderson, D. Parikh, and D. Batra, “Improving vision-and-language navigation with image-text pairs from the web,” in
2020
Earlier work this paper cites.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly,
Original
2020
Earlier work this paper cites.
Z. Kira, “Awesome-llm-robotics,” 2022
2022
Earlier work this paper cites.
S. Parisi, A. Rajeswaran, S. Purushwalkam, and A. Gupta, “The unsurprising effectiveness of pre-trained vision models for control,” in
2022
Earlier work this paper cites.
M. Ahn, A. Brohan, N. Brown, Y. Chebotar, O. Cortes, B. David, C. Finn, C. Fu, K. Gopalakrishnan, K. Hausman,
Original
2022
Earlier work this paper cites.
J.-B. Alayrac, J. Donahue, P. Luc, A. Miech, I. Barr, Y. Hasson, K. Lenc, A. Mensch, K. Millican, M. Reynolds,
2022
Earlier work this paper cites.
K. He, R. Mao, Q. Lin, Y. Ruan, X. Lan, M. Feng, and E. Cambria, “A survey of large language models for healthcare: from data, technology, and applications to accountability and ethics,”
Original
2023
Earlier work this paper cites.
X. Wang, N. Anwer, Y. Dai, and A. Liu, “Chatgpt for design, manufacturing, and education,”
2023
Earlier work this paper cites.
E. Felten, M. Raj, and R. Seamans, “How will language modelers like chatgpt affect occupations and industries?,”
Original
2023
Earlier work this paper cites.
T. Guan, F. Liu, X. Wu, R. Xian, Z. Li, X. Liu, X. Wang, L. Chen, F. Huang, Y. Yacoob,
Original
2023
Earlier work this paper cites.
A. Martino, M. Iannelli, and C. Truong, “Knowledge injection to counter large language model (llm) hallucination,” in
2023
Earlier work this paper cites.
A. Zou, Z. Wang, J. Z. Kolter, and M. Fredrikson, “Universal and transferable adversarial attacks on aligned language models,”
Original
2023
Earlier work this paper cites.
T. Guan, Y. Yang, H. Cheng, M. Lin, R. Kim, R. Madhivanan, A. Sen, and D. Manocha, “Loc-zson: Language-driven object-centric zero-shot object retrieval and navigation,” 2023
2023
Earlier work this paper cites.
V. S. Dorbala, J. F. Mullen Jr, and D. Manocha, “Can an embodied agent find your “cat-shaped mug”? llm-based zero-shot object navigation,”
2023
Earlier work this paper cites.
J. Rintamaki, “Everything-llms-and-robotics,” 2023
2023
Earlier work this paper cites.
C. Huang, O. Mees, A. Zeng, and W. Burgard, “Visual language maps for robot navigation,” in
2023
Earlier work this paper cites.
Y. Jiang, A. Gupta, Z. Zhang, G. Wang, Y. Dou, Y. Chen, L. Fei-Fei, A. Anandkumar, Y. Zhu, and L. Fan, “Vima: General robot manipulation with multimodal prompts,” 2023
2023
Earlier work this paper cites.
M. Shridhar, L. Manuelli, and D. Fox, “Perceiver-actor: A multi-task transformer for robotic manipulation,” in
2023
Earlier work this paper cites.

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Then

A. Robey, Z. Ravichandran, V. Kumar, H. Hassani, and G. J. Pappas, “Jailbreaking llm-controlled robots,”
Original
2024
Closest in time.
H. Fan, X. Liu, J. Y. H. Fuh, W. F. Lu, and B. Li, “Embodied intelligence in manufacturing: leveraging large language models for autonomous industrial robotics,”
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D. Song, J. Liang, A. Payandeh, A. H. Raj, X. Xiao, and D. Manocha, “Vlm-social-nav: Socially aware robot navigation through scoring using vision-language models,”
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K. Weerakoon, M. Elnoor, G. Seneviratne, V. Rajagopal, S. H. Arul, J. Liang, M. K. M. Jaffar, and D. Manocha, “Behav: Behavioral rule guided autonomy using vlms for robot navigation in outdoor scenes,”
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2024

On the Vulnerability of LLM/VLM-Controlled Robotics

Wu, Xiyang, Chakraborty, Souradip, Xian, Ruiqi et al.

Understand

In this work, we highlight vulnerabilities in robotic systems integrating large language models (LLMs) and vision-language models (VLMs) due to input modality sensitivities.

While LLM/VLM-controlled robots show impressive performance across various tasks, their reliability under slight input variations remains underexplored yet critical.
These models are highly sensitive to instruction or perceptual input changes, which can trigger misalignment issues, leading to execution failures with severe real-world consequences.
To study this issue, we analyze the misalignment-induced vulnerabilities within LLM/VLM-controlled robotic systems and present a mathematical formulation for failure modes arising from variations in input modalities.

Built on

C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,”
Original
2013
Earlier work this paper cites.
A. Majumdar, A. Shrivastava, S. Lee, P. Anderson, D. Parikh, and D. Batra, “Improving vision-and-language navigation with image-text pairs from the web,” in
2020
Earlier work this paper cites.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly,
Original
2020
Earlier work this paper cites.
Z. Kira, “Awesome-llm-robotics,” 2022
2022
Earlier work this paper cites.
S. Parisi, A. Rajeswaran, S. Purushwalkam, and A. Gupta, “The unsurprising effectiveness of pre-trained vision models for control,” in
2022
Earlier work this paper cites.
M. Ahn, A. Brohan, N. Brown, Y. Chebotar, O. Cortes, B. David, C. Finn, C. Fu, K. Gopalakrishnan, K. Hausman,
Original
2022
Earlier work this paper cites.
J.-B. Alayrac, J. Donahue, P. Luc, A. Miech, I. Barr, Y. Hasson, K. Lenc, A. Mensch, K. Millican, M. Reynolds,
2022
Earlier work this paper cites.
K. He, R. Mao, Q. Lin, Y. Ruan, X. Lan, M. Feng, and E. Cambria, “A survey of large language models for healthcare: from data, technology, and applications to accountability and ethics,”
Original
2023
Earlier work this paper cites.
X. Wang, N. Anwer, Y. Dai, and A. Liu, “Chatgpt for design, manufacturing, and education,”
2023
Earlier work this paper cites.
E. Felten, M. Raj, and R. Seamans, “How will language modelers like chatgpt affect occupations and industries?,”
Original
2023
Earlier work this paper cites.
T. Guan, F. Liu, X. Wu, R. Xian, Z. Li, X. Liu, X. Wang, L. Chen, F. Huang, Y. Yacoob,
Original
2023
Earlier work this paper cites.
A. Martino, M. Iannelli, and C. Truong, “Knowledge injection to counter large language model (llm) hallucination,” in
2023
Earlier work this paper cites.
A. Zou, Z. Wang, J. Z. Kolter, and M. Fredrikson, “Universal and transferable adversarial attacks on aligned language models,”
Original
2023
Earlier work this paper cites.
T. Guan, Y. Yang, H. Cheng, M. Lin, R. Kim, R. Madhivanan, A. Sen, and D. Manocha, “Loc-zson: Language-driven object-centric zero-shot object retrieval and navigation,” 2023
2023
Earlier work this paper cites.
V. S. Dorbala, J. F. Mullen Jr, and D. Manocha, “Can an embodied agent find your “cat-shaped mug”? llm-based zero-shot object navigation,”
2023
Earlier work this paper cites.
J. Rintamaki, “Everything-llms-and-robotics,” 2023
2023
Earlier work this paper cites.
C. Huang, O. Mees, A. Zeng, and W. Burgard, “Visual language maps for robot navigation,” in
2023
Earlier work this paper cites.
Y. Jiang, A. Gupta, Z. Zhang, G. Wang, Y. Dou, Y. Chen, L. Fei-Fei, A. Anandkumar, Y. Zhu, and L. Fan, “Vima: General robot manipulation with multimodal prompts,” 2023
2023
Earlier work this paper cites.
M. Shridhar, L. Manuelli, and D. Fox, “Perceiver-actor: A multi-task transformer for robotic manipulation,” in
2023
Earlier work this paper cites.

Similar

A. Bucker, L. Figueredo, S. Haddadin, A. Kapoor, S. Ma, S. Vemprala, and R. Bonatti, “Latte: Language trajectory transformer,” in
2023
Cited alongside, same era.
A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, X. Chen, K. Choromanski, T. Ding, D. Driess, A. Dubey, C. Finn,
Original
2023
Cited alongside, same era.
F. Liu, X. Wang, W. Yao, J. Chen, K. Song, S. Cho, Y. Yacoob, and D. Yu, “Mmc: Advancing multimodal chart understanding with large-scale instruction tuning,”
Original
2023
Cited alongside, same era.
J. Liang, P. Gao, X. Xiao, A. J. Sathyamoorthy, M. Elnoor, M. Lin, and D. Manocha, “Mtg: Mapless trajectory generator with traversability coverage for outdoor navigation,”
Original
2023
Cited alongside, same era.
A. Padalkar, A. Pooley, A. Jain, A. Bewley, A. Herzog, A. Irpan, A. Khazatsky, A. Rai, A. Singh, A. Brohan,

Then

A. Robey, Z. Ravichandran, V. Kumar, H. Hassani, and G. J. Pappas, “Jailbreaking llm-controlled robots,”
Original
2024
Closest in time.
H. Fan, X. Liu, J. Y. H. Fuh, W. F. Lu, and B. Li, “Embodied intelligence in manufacturing: leveraging large language models for autonomous industrial robotics,”
2024
Closest in time.
D. Song, J. Liang, A. Payandeh, A. H. Raj, X. Xiao, and D. Manocha, “Vlm-social-nav: Socially aware robot navigation through scoring using vision-language models,”
2024
Closest in time.
K. Weerakoon, M. Elnoor, G. Seneviratne, V. Rajagopal, S. H. Arul, J. Liang, M. K. M. Jaffar, and D. Manocha, “Behav: Behavioral rule guided autonomy using vlms for robot navigation in outdoor scenes,”
Original
2024
Closest in time.
D. Song, J. Liang, X. Xiao, and D. Manocha, “Tgs: Trajectory generation and selection using vision language models in mapless outdoor environments,”

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

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Cited alongside, same era.

Y. Fu, Y. Li, W. Xiao, C. Liu, and Y. Dong, “Safety alignment in nlp tasks: Weakly aligned summarization as an in-context attack,”

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Cited alongside, same era.

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2024