33 citations · 152 across the 27 of their papers we have counts for
10 papers · 1 filter
Learning Object-Based State Estimators for Household Robots
Yilun Du, Tomas Lozano-Perez, Leslie Kaelbling
A robot operating in a household makes observations of multiple objects as it moves around over the course of days or weeks. The objects may be moved by inhabitants, but not comple…
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…
Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time
Ferran Alet, Maria Bauza, Kenji Kawaguchi +3
From CNNs to attention mechanisms, encoding inductive biases into neural networks has been a fruitful source of improvement in machine learning. Adding auxiliary losses to the main…
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…
Visual Prediction of Priors for Articulated Object Interaction
Caris Moses, Michael Noseworthy, Leslie Pack Kaelbling +2
Exploration in novel settings can be challenging without prior experience in similar domains. However, humans are able to build on prior experience quickly and efficiently. Childre…