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安全・認証arXiv:2104.06512

ロボティクスにおける信頼と安全の現状と将来

Trust and Safety

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オーストラリアのロボティクス安全基準の現状を整理し、自律システムの普及に伴い従来の事前認証では対応困難になるとして、APIで常時検証する「ASsurance-as-a-Service」型の自動保証の必要性を論じた章。

著者: S. K. Devitt, R. Horne, Z. Assaad, E. Broad, H. Kurniawati, B. Cardier, A. Scott, S. Lazar, M. Gould, C. Adamson, C. Karl, F. Schrever, S. Keay, K. Tranter, E. Shellshear, D. Hunter, M. Brady, T. Putland

分類: cs.RO, cs.HC

原文アブストラクト

Robotics in Australia have a long history of conforming with safety standards and risk managed practices. This chapter articulates the current state of trust and safety in robotics including society's expectations, safety management systems and system safety as well as emerging issues and methods for ensuring safety in increasingly autonomous robotics. The future of trust and safety will combine standards with iterative, adaptive and responsive regulatory and assurance methods for diverse applications of robotics, autonomous systems and artificial intelligence (RAS-AI). Robotics will need novel technical and social approaches to achieve assurance, particularly for game-changing innovations. The ability for users to easily update algorithms and software, which alters the performance of a system, implies that traditional machine assurance performed prior to deployment or sale, will no longer be viable. Moreover, the high frequency of updates implies that traditional certification that requires substantial time will no longer be practical. To alleviate these difficulties, automation of assurance will likely be needed; something like 'ASsurance-as-a-Service' (ASaaS), where APIs constantly ping RAS-AI to ensure abidance with various rules, frameworks and behavioural expectations. There are exceptions to this, such as in contested or communications denied environments, or in underground or undersea mining; and these systems need their own risk assessments and limitations imposed. Indeed, self-monitors are already operating within some systems. To ensure safe operations of future robotics systems, Australia needs to invest in RAS-AI assurance research, stakeholder engagement and continued development and refinement of robust frameworks, methods, guidelines and policy in order to educate and prepare its technology developers, certifiers, and general population.

  1. 1Boston Dynamics、車輪付きAtlasの研究試作機を紹介研究・技術
  2. 2Hugging Face、ロボット学習基盤「LeRobot v0.7.0」を公開 対話操作と大規模データ学習に対応新製品・発表
  3. 3AGIBOTのG2 6台、上海のコンビニで24時間試験 1,730タスクを実施量産・展開
  4. 4Agility Robotics、ナスダックで取引開始 米国初の人型ロボット専業上場企業と報道業界動向
  5. 5UC BerkeleyとNVIDIAの研究者、AIエージェントでロボット制御コードを仮想環境で改善研究・技術
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