VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909517656096768 |
|---|---|
| author | Liang, Yichao Kumar, Nishanth Tang, Hao Weller, Adrian Tenenbaum, Joshua B. Silver, Tom Henriques, João F. Ellis, Kevin |
| author_facet | Liang, Yichao Kumar, Nishanth Tang, Hao Weller, Adrian Tenenbaum, Joshua B. Silver, Tom Henriques, João F. Ellis, Kevin |
| contents | Broadly intelligent agents should form task-specific abstractions that selectively expose the essential elements of a task, while abstracting away the complexity of the raw sensorimotor space. In this work, we present Neuro-Symbolic Predicates, a first-order abstraction language that combines the strengths of symbolic and neural knowledge representations. We outline an online algorithm for inventing such predicates and learning abstract world models. We compare our approach to hierarchical reinforcement learning, vision-language model planning, and symbolic predicate invention approaches, on both in- and out-of-distribution tasks across five simulated robotic domains. Results show that our approach offers better sample complexity, stronger out-of-distribution generalization, and improved interpretability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_23156 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning Liang, Yichao Kumar, Nishanth Tang, Hao Weller, Adrian Tenenbaum, Joshua B. Silver, Tom Henriques, João F. Ellis, Kevin Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Robotics Broadly intelligent agents should form task-specific abstractions that selectively expose the essential elements of a task, while abstracting away the complexity of the raw sensorimotor space. In this work, we present Neuro-Symbolic Predicates, a first-order abstraction language that combines the strengths of symbolic and neural knowledge representations. We outline an online algorithm for inventing such predicates and learning abstract world models. We compare our approach to hierarchical reinforcement learning, vision-language model planning, and symbolic predicate invention approaches, on both in- and out-of-distribution tasks across five simulated robotic domains. Results show that our approach offers better sample complexity, stronger out-of-distribution generalization, and improved interpretability. |
| title | VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Robotics |
| url | https://arxiv.org/abs/2410.23156 |