151 citations · 209 across the 7 of their papers we have counts for
6 papers · 1 filter
Integrated Task and Motion Planning
Caelan Reed Garrett, Rohan Chitnis, Rachel Holladay +4
The problem of planning for a robot that operates in environments containing a large number of objects, taking actions to move itself through the world as well as to change the sta…
Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks
Tom Silver, Rohan Chitnis, Aidan Curtis +3
Real-world planning problems often involve hundreds or even thousands of objects, straining the limits of modern planners. In this work, we address this challenge by learning to pr…
CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs
Rohan Chitnis, Tom Silver, Beomjoon Kim +2
Meta-planning, or learning to guide planning from experience, is a promising approach to improving the computational cost of planning. A general meta-planning strategy is to learn…
Online Bayesian Goal Inference for Boundedly-Rational Planning Agents
Tan Zhi-Xuan, Jordyn L. Mann, Tom Silver +2
People routinely infer the goals of others by observing their actions over time. Remarkably, we can do so even when those actions lead to failure, enabling us to assist others when…
PDDLGym: Gym Environments from PDDL Problems
Tom Silver, Rohan Chitnis
We present PDDLGym, a framework that automatically constructs OpenAI Gym environments from PDDL domains and problems. Observations and actions in PDDLGym are relational, making the…
GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal Babbling
Rohan Chitnis, Tom Silver, Joshua Tenenbaum +2
We address the problem of efficient exploration for transition model learning in the relational model-based reinforcement learning setting without extrinsic goals or rewards. Inspi…