mlsanityData to 14 Oct 2026

A Comprehensive Survey on Physical Risk Control in the Era of Foundation Model-enabled Robotics

arXiv 2505.12583 ↗First posted 19 May 2025cs.RO cs.AI cs.LGcited by 2

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Abstract (arXiv)

Recent Foundation Model-enabled robotics (FMRs) display greatly improved general-purpose skills, enabling more adaptable automation than conventional robotics. Their ability to handle diverse tasks thus creates new opportunities to replace human labor. However, unlike general foundation models, FMRs interact with the physical world, where their actions directly affect the safety of humans and surrounding objects, requiring careful deployment and control. Based on this proposition, our survey comprehensively summarizes robot control approaches to mitigate physical risks by covering all the lifespan of FMRs ranging from pre-deployment to post-accident stage. Specifically, we broadly divide the timeline into the following three phases: (1) pre-deployment phase, (2) pre-incident phase, and (3) post-incident phase. Throughout this survey, we find that there is much room to study (i) pre-incident risk mitigation strategies, (ii) research that assumes physical interaction with humans, and (iii) essential issues of foundation models themselves. We hope that this survey will be a milestone in providing a high-resolution analysis of the physical risks of FMRs and their control, contributing to the realization of a good human-robot relationship.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 63 of its 109 references that are in the map. First posted on arXiv 19 May 2025.

Builds on

63 of 109 references.

References · 63

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38 more
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50 most cited of 63 All 63 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Can VLMs Unlock Semantic Anomaly Detection? A Framework for Structured Reasoningcited by 0
  2. 2026Towards Trustworthy Embodied Intelligence: A Systems Framework and Graded Trustworthiness Levelscited by 0