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<p>The Cognitive Robot Abstract Machine (CRAM) was developed to enable robots to perform everyday manipulation tasks in human environments by transforming underspecified task instructions into context-sensitive, executable actions. Rather than proposing a single planning algorithm, CRAM represents a cognitive architecture grounded in symbolic knowledge representation, generalized action plans, and introspective execution. This paper traces the evolution of the artificial intelligence ideas that shaped CRAM, from early work on frames, scripts, program interpretation and action-centered planning to contemporary developments in cognitive robotics. We show how these ideas are operationalized in the CRAM architecture and how successive extensions, including semantic digital twins, episodic memories, and dual-process reasoning, support robust execution, explanation and adaptation. In addition, we discuss the Virtual Research Building as an experimental substrate that enables systematic and reproducible evaluation of CRAM components. Finally, we outline future challenges for CRAM, highlighting the need to complement its top-down task-driven paradigm with more interaction-centered and enactive approaches for long-term deployment in human environments.</p>

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From Frames to Pouring: The CRAM Cognitive Architecture for Everyday Robot Manipulation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月20日

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  • Michael Beetz,
  • Michaela Kümpel

摘要

The Cognitive Robot Abstract Machine (CRAM) was developed to enable robots to perform everyday manipulation tasks in human environments by transforming underspecified task instructions into context-sensitive, executable actions. Rather than proposing a single planning algorithm, CRAM represents a cognitive architecture grounded in symbolic knowledge representation, generalized action plans, and introspective execution. This paper traces the evolution of the artificial intelligence ideas that shaped CRAM, from early work on frames, scripts, program interpretation and action-centered planning to contemporary developments in cognitive robotics. We show how these ideas are operationalized in the CRAM architecture and how successive extensions, including semantic digital twins, episodic memories, and dual-process reasoning, support robust execution, explanation and adaptation. In addition, we discuss the Virtual Research Building as an experimental substrate that enables systematic and reproducible evaluation of CRAM components. Finally, we outline future challenges for CRAM, highlighting the need to complement its top-down task-driven paradigm with more interaction-centered and enactive approaches for long-term deployment in human environments.