5 papers · 1 filter
REAP: Learning General Rearrangement Heuristics from Motion-Planner Feedback
Daniel Swoboda, Hector Geffner
Robotic rearrangement tasks require planning sequences of actions that fulfill high-level specifications while also being geometrically feasible. Symbolic abstractions make the com…
Learning to Search and Searching to Learn for Generalization in Planning
Michael Aichmüller, Yannik Hesse, Hector Geffner
Combinatorial generalization remains a central challenge in Deep Reinforcement Learning (DRL). Classical planning provides a simple yet challenging setting to study this problem th…
Differentiable Learning of Lifted Action Schemas for Classical Planning
Jonas Reiter, Jakob Elias Gebler, Hector Geffner
Classical planners can effectively solve very large deterministic MDPs represented in STRIPS or PDDL where states are sets of atoms over objects and relations, and lifted action sc…
Efficient Lookahead Encoding and Abstracted Width for Learning General Policies in Classical Planning
Michael Aichmüller, Simon Ståhlberg, Martin Funkquist +1
Generalized planning aims to learn policies that generalize across collections of instances within a classical planning domain. Recent Graph Neural Network (GNN) approaches have le…
Learning Lifted Action Models from Traces with Minimal Information About Actions and States
Jonas Gösgens, Niklas Jansen, Hector Geffner
It has been recently shown that lifted STRIPS models can be learned correctly and efficiently from action traces alone; i.e., applicable action sequences from a hidden STRIPS model…