Maria Bauza | DeepAI

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∙ 07/24/2023

simPLE: a visuotactile method learned in simulation to precisely pick, localize, regrasp, and place objects

Existing robotic systems have a clear tension between generality and pre...

0 Maria Bauza, et al. ∙

∙ 06/20/2023

RoboCat: A Self-Improving Foundation Agent for Robotic Manipulation

The ability to leverage heterogeneous robotic experience from different ...

0 Konstantinos Bousmalis, et al. ∙

∙ 03/14/2023

FingerSLAM: Closed-loop Unknown Object Localization and Reconstruction from Visuo-tactile Feedback

In this paper, we address the problem of using visuo-tactile feedback fo...

0 Jialiang Zhao, et al. ∙

∙ 04/25/2022

Tac2Pose: Tactile Object Pose Estimation from the First Touch

In this paper, we present Tac2Pose, an object-specific approach to tacti...

4 Maria Bauza, et al. ∙

∙ 12/09/2020

Tactile Object Pose Estimation from the First Touch with Geometric Contact Rendering

In this paper, we present an approach to tactile pose estimation from th...

0 Maria Bauza, et al. ∙

∙ 11/13/2020

Tactile SLAM: Real-time inference of shape and pose from planar pushing

Tactile perception is central to robot manipulation in unstructured envi...

0 Sudharshan Suresh, et al. ∙

∙ 11/12/2019

Experience-Embedded Visual Foresight

Visual foresight gives an agent a window into the future, which it can u...

9 Lin Yen-Chen, et al. ∙

∙ 11/08/2019

Accurate Vision-based Manipulation through Contact Reasoning

Planning contact interactions is one of the core challenges of many robo...

18 Alina Kloss, et al. ∙

∙ 10/01/2019

Omnipush: accurate, diverse, real-world dataset of pushing dynamics with RGB-D video

Pushing is a fundamental robotic skill. Existing work has shown how to e...

7 Maria Bauza, et al. ∙

∙ 04/24/2019

Tactile Mapping and Localization from High-Resolution Tactile Imprints

This work studies the problem of shape reconstruction and object localiz...

0 Maria Bauza, et al. ∙

∙ 04/18/2019

Graph Element Networks: adaptive, structured computation and memory

We explore the use of graph neural networks (GNNs) to model spatial proc...

30 Ferran Alet, et al. ∙

∙ 04/13/2019

Combining Physical Simulators and Object-Based Networks for Control

Physics engines play an important role in robot planning and control; ho...

0 Anurag Ajay, et al. ∙

∙ 12/19/2018

Modular meta-learning in abstract graph networks for combinatorial generalization

Modular meta-learning is a new framework that generalizes to unseen data...

0 Ferran Alet, et al. ∙

∙ 08/09/2018

Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing

An efficient, generalizable physical simulator with universal uncertaint...

0 Anurag Ajay, et al. ∙

∙ 07/26/2018

A Data-Efficient Approach to Precise and Controlled Pushing

Decades of research in control theory have shown that simple controllers...

0 Francois R. Hogan, et al. ∙

∙ 03/05/2018

Tactile Regrasp: Grasp Adjustments via Simulated Tactile Transformations

This paper presents a novel regrasp control policy that makes use of tac...

0 Francois R. Hogan, et al. ∙

∙ 10/03/2017

Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching

This paper presents a robotic pick-and-place system that is capable of g...

0 Andy Zeng, et al. ∙

∙ 09/23/2017

GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs

This work centers on the problem of stochastic filtering for systems tha...

0 Maria Bauza, et al. ∙

∙ 04/10/2017

A probabilistic data-driven model for planar pushing

This paper presents a data-driven approach to model planar pushing inter...

0 Maria Bauza, et al. ∙

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