Systems Challenges for Trustworthy Embodied Systems
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Abstract (arXiv)
A new generation of increasingly autonomous and self-learning embodied systems is about to be developed. When deploying embodied systems into a real-life context we face various engineering challenges, as it is crucial to coordinate the behavior of embodied systems in a beneficial manner, ensure their compatibility with our human-centered social values, and design verifiably safe and reliable human-machine interaction. We are arguing that traditional systems engineering is coming to a climacteric from embedded to embodied systems, and with assuring the trustworthiness of dynamic federations of situationally aware, intent-driven, explorative, ever-evolving, largely non-predictable, and increasingly autonomous embodied systems in uncertain, complex, and unpredictable real-world contexts. We are therefore identifying a number of urgent systems challenges for trustworthy embodied systems, including robust and human-centric AI, cognitive architectures, uncertainty quantification, trustworthy self-integration, and continual analysis and assurance.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 8 of its 51 references that are in the map. First posted on arXiv 10 Jan 2022.
Builds on
8 of 51 references.
References · 8
- 2017Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networkscited by 744
- 2016Safety Verification of Deep Neural Networkscited by 263
- 2018Reachability Analysis of Deep Neural Networks with Provable Guaranteescited by 93
- 2017Verifying Properties of Binarized Deep Neural Networkscited by 79
- 2018SOTER: A Runtime Assurance Framework for Programming Safe Robotics Systemscited by 25
- 2021Parallel and Multi-Objective Falsification with Scenic and VerifAIcited by 8
- 2020Assurance 2.0: A Manifestocited by 6
- 2018Reasoning about Safety of Learning-Enabled Components in Autonomous Cyber-physical Systemscited by 3
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Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 1.
2 papers.