100 citations · 399 across the 42 of their papers we have counts for
7 papers · 1 filter
Online Abstraction with MDP Homomorphisms for Deep Learning
Ondrej Biza, Robert Platt
Abstraction of Markov Decision Processes is a useful tool for solving complex problems, as it can ignore unimportant aspects of an environment, simplifying the process of learning…
AutOTranS: an Autonomous Open World Transportation System
Brayan S. Zapata-Impata, Vikrant Shah, Hanumant Singh +1
Tasks in outdoor open world environments are now ripe for automation with mobile manipulators. The dynamic, unstructured and unknown environments associated with such tasks -- a pr…
Towards Assistive Robotic Pick and Place in Open World Environments
Dian Wang, Colin Kohler, Andreas ten Pas +4
Assistive robot manipulators must be able to autonomously pick and place a wide range of novel objects to be truly useful. However, current assistive robots lack this capability. A…
Adapting control policies from simulation to reality using a pairwise loss
Ulrich Viereck, Xingchao Peng, Kate Saenko +1
This paper proposes an approach to domain transfer based on a pairwise loss function that helps transfer control policies learned in simulation onto a real robot. We explore the id…
Deictic Image Maps: An Abstraction For Learning Pose Invariant Manipulation Policies
Robert Platt, Colin Kohler, Marcus Gualtieri
In applications of deep reinforcement learning to robotics, it is often the case that we want to learn pose invariant policies: policies that are invariant to changes in the positi…
Learning 6-DoF Grasping and Pick-Place Using Attention Focus
Marcus Gualtieri, Robert Platt
We address a class of manipulation problems where the robot perceives the scene with a depth sensor and can move its end effector in a space with six degrees of freedom -- 3D posit…