Inventing Relational State and Action Abstractions for Effective and Efficient Bilevel Planning

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Do As I Can, Not As I Say: Grounding Language in Robotic Affordances (2022, methodology): “Machine learning has recently been applied to enable abstract task specification, allow general primitives, or relax constraints [65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76].”
Inner Monologue: Embodied Reasoning through Planning with Language Models (2022, background): “Traditionally, this problem has been solved through optimization [28, 29] or symbolic reasoning [24, 26], but more recently machine learning has been applied to aspects of the problem via learned representations, learned task-primitives, and more [30, 31, 32, 33, 34, 35, 36, 37, 38].”
Inductive biases for deep learning of higher-level cognition (2020, background): “knowledge is generic, defined over abstract variables, and can be applied on different instances factorizing knowledge in terms of abstract variables and functions that encapsulate how these variables interact with each other [99,114,115]”
ProgPrompt: Generating Situated Robot Task Plans using Large Language Models (2022, background): “Recent works have explored learning-based task & motion planning, using methods such as representation learning, hierarchical learning, language as planning space, learning compositional skills and more [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26].”