Towards Trustworthy Embodied Intelligence: A Systems Framework and Graded Trustworthiness Levels
Xinyu Yang, Tianxing Chen, Honghao Su, Minxuan Wang, Chenze Yu, Zhangzheng Tu, Yue Chen, Yuxiao Huo, Lingfeng Zhang, Yan Huang, Yan Qin, Shaolong Zhu, Qiwei Liang, Hekun Tian, Shujia Liu, Guangyu Chen, Junhao Gong, Zixuan Li, Wenwei Lin, Zijian Lin, Wenxuan Zhu, Eric J Chen, Yue Yuan, Qize Yu, Jiaqi Liang, Haowen Yan, Hengfei Zhao, Weijie Wan, Zikun Xiao, Junyuan Tang, Baijun Chen, Kai-Chong Lei, Kaixuan Wang, Kailun Su, Zanxin Chen, Yao Mu, Renjing Xu, Chuqiao Lyu, Qi Xiong, Ping Luo, Wenbo Ding
Why It Matters
What makes this one worth your time
As embodied AI systems become more prevalent, establishing a clear framework for trustworthiness is crucial for ensuring safety and reliability in real-world applications.
A comprehensive framework for assessing trustworthiness in embodied intelligence systems.
Summary
The paper proposes a systems framework for trustworthy embodied intelligence, defining trustworthiness in terms of sustained safe success and outlining four interdependent layers that contribute to this objective.
Key contributions
- Definition of trustworthy embodied intelligence and sustained safe success.
- Development of a four-layered systems framework for ensuring trustworthiness.
- Introduction of a hierarchy of trustworthiness levels for comparative evaluation.
Notable insights
- The model layer's focus on calibrated uncertainty and explicit safety preferences is a novel approach to enhancing trustworthiness.
- The proposed non-normative hierarchy of trustworthiness levels provides a structured way to evaluate and compare different systems.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2607.26121v1 Announce Type: cross Abstract: Embodied intelligence integrates learned perception and decision making with real-time computation, control, and physical interaction. Because failures can cause immediate physical or operational harm, task completion alone does not establish trustworthiness. We define trustworthy embodied intelligence as the sustained capacity to execute specified tasks reliably under environmental and system variation while maintaining risk within acceptable bounds. We term this objective sustained safe success. Its supporting mechanisms are organized into four interdependent layers. The model layer generates task-competent action proposals with calibrated uncertainty and explicit safety preferences. The system layer realizes authorized actions dependably through integrated sensing, computation, control, hardware safeguards, fault containment, and fallback. The evidence layer substantiates bounded claims through evaluation, verification, validation, traceability, and structured assurance arguments. The deployment layer maintains claim validity through runtime monitoring, authority management, intervention, incident response, and controlled updates. Because assumptions and failures propagate across these layers, neither model capability, isolated safeguards, nor benchmark performance alone can establish end-to-end trustworthiness. Drawing on embodied AI, robotics, control, dependable computing, distributed systems, and autonomous driving, we further propose a non-normative hierarchy of trustworthiness levels. This hierarchy grades the strength of bounded deployment claims across task capability, safety, system assurance, operational governance, and supporting evidence, providing a basis for bounded deployment, comparative evaluation, research prioritization, and future standardization.