Sketch-Plan-Generalize: Continual Few-Shot Learning of Inductively Generalizable Spatial Concepts

• URL: http://arxiv.org/abs/2404.07774v2
• Date: Wed, 29 May 2024 09:46:39 GMT
• Title: Sketch-Plan-Generalize: Continual Few-Shot Learning of Inductively Generalizable Spatial Concepts
• Authors: Namasivayam Kalithasan, Sachit Sachdeva, Himanshu Gaurav Singh, Vishal Bindal, Arnav Tuli, Gurarmaan Singh Panjeta, Divyanshu Aggarwal, Rohan Paul, Parag Singla,
• Abstract summary: We seek a learning architecture that infers a succinct $program$ representation that explains the observed instance. Our approach combines the benefits of the code generation ability of large language models along with grounded neural representations.
• Score: 6.932008652560561
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Our goal is to enable embodied agents to learn inductively generalizable spatial concepts, e.g., learning staircase as an inductive composition of towers of increasing height. Given a human demonstration, we seek a learning architecture that infers a succinct ${program}$ representation that explains the observed instance. Additionally, the approach should generalize inductively to novel structures of different sizes or complex structures expressed as a hierarchical composition of previously learned concepts. Existing approaches that use code generation capabilities of pre-trained large (visual) language models, as well as purely neural models, show poor generalization to a-priori unseen complex concepts. Our key insight is to factor inductive concept learning as (i) ${\it Sketch:}$ detecting and inferring a coarse signature of a new concept (ii) ${\it Plan:}$ performing MCTS search over grounded action sequences (iii) ${\it Generalize:}$ abstracting out grounded plans as inductive programs. Our pipeline facilitates generalization and modular reuse, enabling continual concept learning. Our approach combines the benefits of the code generation ability of large language models (LLM) along with grounded neural representations, resulting in neuro-symbolic programs that show stronger inductive generalization on the task of constructing complex structures in relation to LLM-only and neural-only approaches. Furthermore, we demonstrate reasoning and planning capabilities with learned concepts for embodied instruction following.

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