From Real World to Logic and Back: Learning Generalizable Relational Concepts For Long Horizon Robot Planning
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909823516278784 |
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| author | Shah, Naman Nagpal, Jayesh Srivastava, Siddharth |
| author_facet | Shah, Naman Nagpal, Jayesh Srivastava, Siddharth |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_11871 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From Real World to Logic and Back: Learning Generalizable Relational Concepts For Long Horizon Robot Planning Shah, Naman Nagpal, Jayesh Srivastava, Siddharth Robotics Artificial Intelligence 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. |
| title | From Real World to Logic and Back: Learning Generalizable Relational Concepts For Long Horizon Robot Planning |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2402.11871 |