b'Robert Platt'

research

∙ 11/16/2022

The Surprising Effectiveness of Equivariant Models in Domains with Latent Symmetry

Extensive work has demonstrated that equivariant neural networks can sig...

Dian Wang, et al. ∙

research

∙ 11/09/2022

Grasp Learning: Models, Methods, and Performance

Grasp learning has become an exciting and important topic in robotics. J...

Robert Platt, et al. ∙

research

∙ 11/03/2022

Leveraging Fully Observable Policies for Learning under Partial Observability

Reinforcement learning in partially observable domains is challenging du...

Hai Nguyen, et al. ∙

research

∙ 10/31/2022

SEIL: Simulation-augmented Equivariant Imitation Learning

In robotic manipulation, acquiring samples is extremely expensive becaus...

Mingxi Jia, et al. ∙

research

∙ 10/31/2022

Edge Grasp Network: A Graph-Based SE(3)-invariant Approach to Grasp Detection

Given point cloud input, the problem of 6-DoF grasp pose detection is to...

Haojie Huang, et al. ∙

research

∙ 07/22/2022

Graph-Structured Policy Learning for Multi-Goal Manipulation Tasks

Multi-goal policy learning for robotic manipulation is challenging. Prio...

David Klee, et al. ∙

research

∙ 07/18/2022

I2I: Image to Icosahedral Projection for SO(3) Object Reasoning from Single-View Images

Reasoning about 3D objects based on 2D images is challenging due to larg...

David Klee, et al. ∙

research

∙ 06/29/2022

Visual Foresight With a Local Dynamics Model

Model-free policy learning has been shown to be capable of learning mani...

Colin Kohler, et al. ∙

research

∙ 06/02/2022

Deep Transformer Q-Networks for Partially Observable Reinforcement Learning

Real-world reinforcement learning tasks often involve some form of parti...

Kevin Esslinger, et al. ∙

research

∙ 05/28/2022

BulletArm: An Open-Source Robotic Manipulation Benchmark and Learning Framework

We present BulletArm, a novel benchmark and learning-environment for rob...

Dian Wang, et al. ∙

research

∙ 04/03/2022

Efficient and Accurate Candidate Generation for Grasp Pose Detection in SE(3)

Grasp detection of novel objects in unstructured environments is a key c...

Andreas ten Pas, et al. ∙

research

∙ 04/02/2022

Hierarchical Reinforcement Learning under Mixed Observability

The framework of mixed observable Markov decision processes (MOMDP) mode...

Hai Nguyen, et al. ∙

research

∙ 03/21/2022

Tactile Pose Estimation and Policy Learning for Unknown Object Manipulation

Object pose estimation methods allow finding locations of objects in uns...

Tarik Kelestemur, et al. ∙

research

∙ 03/09/2022

On-Robot Policy Learning with O(2)-Equivariant SAC

Recently, equivariant neural network models have been shown to be useful...

Dian Wang, et al. ∙

research

∙ 03/08/2022

SO(2)-Equivariant Reinforcement Learning

Equivariant neural networks enforce symmetry within the structure of the...

Dian Wang, et al. ∙

research

∙ 02/18/2022

Sample Efficient Grasp Learning Using Equivariant Models

In planar grasp detection, the goal is to learn a function from an image...

Xupeng Zhu, et al. ∙

research

∙ 02/18/2022

Equivariant Transporter Network

Transporter Net is a recently proposed framework for pick and place that...

Haojie Huang, et al. ∙

research

∙ 01/20/2022

GASCN: Graph Attention Shape Completion Network

Shape completion, the problem of inferring the complete geometry of an o...

Haojie Huang, et al. ∙

research

∙ 12/15/2021

A Targeted Approach to Confounder Selection for High-Dimensional Data

We consider the problem of selecting confounders for adjustment from a p...

Asad Haris, et al. ∙

research

∙ 10/28/2021

Equivariant Q Learning in Spatial Action Spaces

Recently, a variety of new equivariant neural network model architecture...

Dian Wang, et al. ∙

research

∙ 01/11/2021

Action Priors for Large Action Spaces in Robotics

In robotics, it is often not possible to learn useful policies using pur...

Ondrej Biza, et al. ∙

research

∙ 11/11/2020

Learning Bayes Filter Models for Tactile Localization

Localizing and tracking the pose of robotic grippers are necessary skill...

Tarik Kelestemur, et al. ∙

research

∙ 10/19/2020

Belief-Grounded Networks for Accelerated Robot Learning under Partial Observability

Many important robotics problems are partially observable in the sense t...

Hai Nguyen, et al. ∙

research

∙ 10/15/2020

Pick-Place With Uncertain Object Instance Segmentation and Shape Completion

In this paper we consider joint perception and control of a pick-place s...

Marcus Gualtieri, et al. ∙

research

∙ 10/06/2020

Policy learning in SE(3) action spaces

In the spatial action representation, the action space spans the space o...

Dian Wang, et al. ∙

research

∙ 05/24/2020

Learning visual servo policies via planner cloning

Learning control policies for visual servoing in novel environments is a...

Ulrich Viereck, et al. ∙

research

∙ 03/09/2020

Learning discrete state abstractions with deep variational inference

Abstraction is crucial for effective sequential decision making in domai...

Ondrej Biza, et al. ∙

research

∙ 04/19/2019

Learning Manipulation Skills Via Hierarchical Spatial Attention

Learning generalizable skills in robotic manipulation has long been chal...

Marcus Gualtieri, et al. ∙

research

∙ 11/30/2018

Online abstraction with MDP homomorphisms for Deep Learning

Abstraction of Markov Decision Processes is a useful tool for solving co...

Ondrej Biza, et al. ∙

research

∙ 10/08/2018

AutOTranS: an Autonomous Open World Transportation System

Tasks in outdoor open world environments are now ripe for automation wit...

Brayan S. Zapata-Impata, et al. ∙

research

∙ 09/25/2018

A Scooter-Mounted Robot Arm to Assist with Activities of Daily Life

Many people with motor disabilities struggle with activities of daily li...

Dian Wang, et al. ∙

research

∙ 07/27/2018

Adapting control policies from simulation to reality using a pairwise loss

This paper proposes an approach to domain transfer based on a pairwise l...

Ulrich Viereck, et al. ∙

research

∙ 06/26/2018

Deictic Image Maps: An Abstraction For Learning Pose Invariant Manipulation Policies

In applications of deep reinforcement learning to robotics, it is often ...

Robert Platt, et al. ∙

research

∙ 06/15/2018

Learning 6-DoF Grasping and Pick-Place Using Attention Focus

We address a class of manipulation problems where the robot perceives th...

Marcus Gualtieri, et al. ∙

research

∙ 05/21/2018

Hierarchical Reinforcement Learning with Hindsight

Reinforcement Learning (RL) algorithms can suffer from poor sample effic...

Andrew Levy, et al. ∙

research

∙ 12/04/2017

Hierarchical Actor-Critic

We present a novel approach to hierarchical reinforcement learning calle...

Andrew Levy, et al. ∙

research

∙ 06/14/2017

Learning a visuomotor controller for real world robotic grasping using simulated depth images

We want to build robots that are useful in unstructured real world appli...

Ulrich Viereck, et al. ∙

Robert Platt

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