100 citations · 399 across the 42 of their papers we have counts for
6 papers · 1 filter
Learning Bayes Filter Models for Tactile Localization
Tarik Kelestemur, Colin Keil, John P. Whitney +2
Localizing and tracking the pose of robotic grippers are necessary skills for manipulation tasks. However, the manipulators with imprecise kinematic models (e.g. low-cost arms) or…
Policy learning in SE(3) action spaces
Dian Wang, Colin Kohler, Robert Platt
In the spatial action representation, the action space spans the space of target poses for robot motion commands, i.e. SE(2) or SE(3). This approach has been used to solve challeng…
Belief-Grounded Networks for Accelerated Robot Learning under Partial Observability
Hai Nguyen, Brett Daley, Xinchao Song +2
Many important robotics problems are partially observable in the sense that a single visual or force-feedback measurement is insufficient to reconstruct the state. Standard approac…
Robotic Pick-and-Place With Uncertain Object Instance Segmentation and Shape Completion
Marcus Gualtieri, Robert Platt
We consider robotic pick-and-place of partially visible, novel objects, where goal placements are non-trivial, e.g., tightly packed into a bin. One approach is (a) use object insta…
Learning visual servo policies via planner cloning
Ulrich Viereck, Kate Saenko, Robert Platt
Learning control policies for visual servoing in novel environments is an important problem. However, standard model-free policy learning methods are slow. This paper explores plan…
Learning Discrete State Abstractions With Deep Variational Inference
Ondrej Biza, Robert Platt, Jan-Willem van de Meent +1
Abstraction is crucial for effective sequential decision making in domains with large state spaces. In this work, we propose an information bottleneck method for learning approxima…