🤖 AI Summary
This work addresses the bottleneck in hierarchical planning for continuous-state/action domains—namely, its reliance on manually engineered symbolic predicates for state abstraction. We propose the first fully automated predicate invention framework. Methodologically, it employs grammar-guided differentiable search over predicate sets, jointly optimizing high-level operators and low-level samplers via proxy objective optimization and demonstration-guided symbolic predicate learning—enabling end-to-end abstraction discovery and hierarchical planning co-training. Evaluated on four robotic planning benchmarks, our approach significantly outperforms six baselines, demonstrating strong generalization: it rapidly solves unseen tasks. Our core contributions are twofold: (1) the first fully automated, human-intervention-free predicate invention mechanism; and (2) empirical validation that automatically discovered predicates substantially improve both planning efficiency and cross-task generalization.
📝 Abstract
Efficient planning in continuous state and action spaces is fundamentally hard, even when the transition model is deterministic and known. One way to alleviate this challenge is to perform bilevel planning with abstractions, where a high-level search for abstract plans is used to guide planning in the original transition space. Previous work has shown that when state abstractions in the form of symbolic predicates are hand-designed, operators and samplers for bilevel planning can be learned from demonstrations. In this work, we propose an algorithm for learning predicates from demonstrations, eliminating the need for manually specified state abstractions. Our key idea is to learn predicates by optimizing a surrogate objective that is tractable but faithful to our real efficient-planning objective. We use this surrogate objective in a hill-climbing search over predicate sets drawn from a grammar. Experimentally, we show across four robotic planning environments that our learned abstractions are able to quickly solve held-out tasks, outperforming six baselines.
