This paper presents a hybrid robot cognitive architecture, CRAM, that enables robot agents to accomplish everyday manipulation tasks. It addresses five key challenges that arise when carrying out everyday activities. These include (i) the underdetermined nature of task specification, (ii) the generation of context-specific behavior, (iii) the ability to make decisions based on knowledge, experience, and prediction, (iv) the ability to reason at the levels of motions and sensor data, and (v) the ability to explain actions and the consequences of these actions. We explore the computational foundations of the CRAM cognitive model: the self-programmability entailed by physical symbol systems, the CRAM plan language, generalized action plans and implicit-to-explicit manipulation, generative models, digital twin knowledge representation & reasoning, and narrative-enabled episodic memories. We describe the structure of the cognitive architecture and explain the process by which CRAM transforms generalized action plans into parameterized motion plans. It does this using knowledge and reasoning to identify the parameter values that maximize the likelihood of successfully accomplishing the action. We demonstrate the ability of a CRAM-controlled robot to carry out everyday activities in a kitchen environment. Finally, we consider future extensions that focus on achieving greater flexibility through transformational learning and metacognition.

翻译：本文提出了一种混合机器人认知架构CRAM，使其能够完成日常操作任务。该架构解决了日常活动中出现的五个关键挑战，包括：(i) 任务规范的不确定性本质，(ii) 情境特定行为的生成，(iii) 基于知识、经验和预测的决策能力，(iv) 在运动与传感器数据层面的推理能力，以及(v) 解释行动及其后果的能力。我们探索了CRAM认知模型的计算基础：物理符号系统的自编程能力、CRAM规划语言、广义动作规划与隐式到显式操控、生成模型、数字孪生知识表示与推理，以及基于叙事的场景记忆。本文描述了该认知架构的结构，并阐释了CRAM将广义动作规划转化为参数化运动规划的过程——通过知识推理识别使行动成功概率最大化的参数值。我们展示了受CRAM控制的机器人在厨房环境中执行日常活动的能力。最后，展望了通过变换学习与元认知实现更高灵活性的未来拓展方向。