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arXiv · 2402.11871

From Real World to Logic and Back: Learning Generalizable Relational Concepts For Long Horizon Robot Planning

Naman Shah·Jayesh Nagpal·Siddharth Srivastava
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Abstract

Robots still lag behind humans in their ability to generalize from limited experience, particularly when transferring learned behaviors to long-horizon tasks in unseen environments. We present the first method that enables robots to autonomously invent symbolic, relational concepts directly from a small number of raw, unsegmented, and unannotated demonstrations. From these, the robot learns logic-based world models that support zero-shot generalization to tasks of far greater complexity than those in training. Our framework achieves performance on par with hand-engineered symbolic models, while scaling to execution horizons far beyond training and handling up to 18$\times$ more objects than seen during learning. The results demonstrate a framework for autonomously acquiring transferable symbolic abstractions from raw robot experience, contributing toward the development of interpretable, scalable, and generalizable robot planning systems. Project website and code: https://aair-lab.github.io/r2l-lamp.

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Naman Shah, Jayesh Nagpal, Siddharth Srivastava. 2025-10-03. From Real World to Logic and Back: Learning Generalizable Relational Concepts For Long Horizon Robot Planning. https://arxiv.org/abs/2402.11871

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