Recent work has considered whether large language models (LLMs) can func...
State abstraction is an effective technique for planning in robotics
env...
Bilevel planning, in which a high-level search over an abstraction of an...
Decision-making is challenging in robotics environments with continuous
...
A longstanding objective in classical planning is to synthesize policies...
Effective and efficient planning in continuous state and action spaces i...
Recent advances in reinforcement learning (RL) have led to a growing int...
Generalized planning accelerates classical planning by finding an
algori...
Despite recent, independent progress in model-based reinforcement learni...
Robotic planning problems in hybrid state and action spaces can be solve...
The problem of planning for a robot that operates in environments contai...
Real-world planning problems often involve hundreds or even thousands of...
Meta-planning, or learning to guide planning from experience, is a promi...
People routinely infer the goals of others by observing their actions ov...
We present PDDLGym, a framework that automatically constructs OpenAI Gym...
We address the problem of efficient exploration for learning lifted oper...
We describe an expressive class of policies that can be efficiently lear...
We present Residual Policy Learning (RPL): a simple method for improving...
The recent adaptation of deep neural network-based methods to reinforcem...