mlsanityData to 13 Oct 2026

Generative AI Agents in Autonomous Machines: A Safety Perspective

arXiv 2410.15489 ↗First posted 20 Oct 2024cs.RO cs.AI cs.LGcited by 7

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Abstract (arXiv)

The integration of Generative Artificial Intelligence (AI) into autonomous machines represents a major paradigm shift in how these systems operate and unlocks new solutions to problems once deemed intractable. Although generative AI agents provide unparalleled capabilities, they also have unique safety concerns. These challenges require robust safeguards, especially for autonomous machines that operate in high-stakes environments. This work investigates the evolving safety requirements when generative models are integrated as agents into physical autonomous machines, comparing these to safety considerations in less critical AI applications. We explore the challenges and opportunities to ensure the safe deployment of generative AI-driven autonomous machines. Furthermore, we provide a forward-looking perspective on the future of AI-driven autonomous systems and emphasize the importance of evaluating and communicating safety risks. As an important step towards addressing these concerns, we recommend the development and implementation of comprehensive safety scorecards for the use of generative AI technologies in autonomous machines.

Key facts. Cited by 7 papers in the map (data through 13 Oct 2026). Builds on 125 of its 204 references that are in the map. First posted on arXiv 20 Oct 2024.

Builds on

125 of 204 references.

References · 125

  1. 2017Attention Is All You Needcited by 60,106
  2. 2020Language Models are Few-Shot Learnerscited by 32,320
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,043
  4. 2020Denoising Diffusion Probabilistic Modelscited by 20,952
  5. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,656
  6. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,979
  7. 2020Denoising Diffusion Implicit Modelscited by 9,718
  8. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,213
  9. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,564
  10. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,000
  11. 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,628
  12. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,516
38 more
  1. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,463
  2. 2023Qwen Technical Reportcited by 3,310
  3. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,294
  4. 2023RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Controlcited by 3,022
  5. 2014Sequence to Sequence Learning with Neural Networkscited by 2,981
  6. 2023Diffusion Policy: Visuomotor Policy Learning via Action Diffusioncited by 2,760
  7. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,619
  8. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,586
  9. 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,321
  10. 2022Video Diffusion Modelscited by 2,293
  11. 2014Conditional Generative Adversarial Netscited by 2,157
  12. 2023PaLM-E: An Embodied Multimodal Language Modelcited by 2,052
  13. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,906
  14. 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,815
  15. 2022Imagen Video: High Definition Video Generation with Diffusion Modelscited by 1,768
  16. 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,473
  17. 2024Octo: An Open-Source Generalist Robot Policycited by 1,450
  18. 2022Competition-Level Code Generation with AlphaCodecited by 1,445
  19. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,101
  20. 2018Model Cards for Model Reportingcited by 1,076
  21. 2022Inner Monologue: Embodied Reasoning through Planning with Language Modelscited by 1,065
  22. 2022Planning with Diffusion for Flexible Behavior Synthesiscited by 1,035
  23. 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,033
  24. 2023VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Modelscited by 739
  25. 2016Generative Adversarial Text to Image Synthesiscited by 735
  26. 2022What Can Transformers Learn In-Context? A Case Study of Simple Function Classescited by 706
  27. 2023Measuring Faithfulness in Chain-of-Thought Reasoningcited by 689
  28. 2022Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulationcited by 546
  29. 2022GIT: A Generative Image-to-text Transformer for Vision and Languagecited by 536
  30. 2017Towards Principled Methods for Training Generative Adversarial Networkscited by 464
  31. 2023Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignmentcited by 400
  32. 2024RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robotscited by 397
  33. 2023Shap-E: Generating Conditional 3D Implicit Functionscited by 383
  34. 2022Visual Language Maps for Robot Navigationcited by 363
  35. 2022CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learningcited by 348
  36. 2023Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Priorcited by 342
  37. 2018Generating Wikipedia by Summarizing Long Sequencescited by 335
  38. 2021Multimodal datasets: misogyny, pornography, and malignant stereotypescited by 327

50 most cited of 125 All 125 (JSON)

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 5; 2026: 2.

7 papers.

  1. 2025SoK: Trust-Authorization Mismatch in LLM Agent Interactionscited by 11
  2. 2025Real-Time Out-of-Distribution Failure Prevention via Multi-Modal Reasoningcited by 10
  3. 2025Enhancing Robustness of LLM-Driven Multi-Agent Systems through Randomized Smoothingcited by 9
  4. 2025Don't Run with Scissors: Pruning Breaks VLA Models but They Can Be Recoveredcited by 8
  5. 2026Towards trustworthy agentic AI: a comprehensive survey of safety, robustness, privacy, and system securitycited by 5
  6. 2025Enforcement Agents: Enhancing Accountability and Resilience in Multi-Agent AI Frameworkscited by 4
  7. 2026From Prompts to Context: An Ontology-Driven Framework for Human-Generative AI Collaborationcited by 0