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

• URL: http://arxiv.org/abs/2402.11871v6
• Date: Fri, 03 Oct 2025 19:35:31 GMT
• Title: From Real World to Logic and Back: Learning Generalizable Relational Concepts For Long Horizon Robot Planning
• Authors: Naman Shah, Jayesh Nagpal, Siddharth Srivastava,
• Abstract summary: We present a method that enables robots to invent symbolic, relational concepts directly from a small number of raw, unsegmented, and unannotated demonstrations.<n>Our framework achieves performance on par with hand-engineered symbolic models, while scaling to execution horizons far beyond training.
• Score: 16.115874470700113
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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