141 citations · 638 across the 52 of their papers we have counts for
6 papers · 1 filter
Online Replanning in Belief Space for Partially Observable Task and Motion Problems
Caelan Reed Garrett, Chris Paxton, Tomás Lozano-Pérez +2
To solve multi-step manipulation tasks in the real world, an autonomous robot must take actions to observe its environment and react to unexpected observations. This may require op…
Omnipush: accurate, diverse, real-world dataset of pushing dynamics with RGB-D video
Maria Bauza, Ferran Alet, Yen-Chen Lin +4
Pushing is a fundamental robotic skill. Existing work has shown how to exploit models of pushing to achieve a variety of tasks, including grasping under uncertainty, in-hand manipu…
Learning Compact Models for Planning with Exogenous Processes
Rohan Chitnis, Tomás Lozano-Pérez
We address the problem of approximate model minimization for MDPs in which the state is partitioned into endogenous and (much larger) exogenous components. An exogenous state varia…
Differentiable Algorithm Networks for Composable Robot Learning
Peter Karkus, Xiao Ma, David Hsu +3
This paper introduces the Differentiable Algorithm Network (DAN), a composable architecture for robot learning systems. A DAN is composed of neural network modules, each encoding a…
Graph Element Networks: adaptive, structured computation and memory
Ferran Alet, Adarsh K. Jeewajee, Maria Bauza +3
We explore the use of graph neural networks (GNNs) to model spatial processes in which there is no a priori graphical structure. Similar to finite element analysis, we assign nodes…
Look before you sweep: Visibility-aware motion planning
Gustavo Goretkin, Leslie Pack Kaelbling, Tomás Lozano-Pérez
This paper addresses the problem of planning for a robot with a directional obstacle-detection sensor that must move through a cluttered environment. The planning objective is to r…