Search Results for author: Maria Bauza

Found 18 papers, 4 papers with code

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

no code implementations14 Mar 2023 Jialiang Zhao, Maria Bauza, Edward H. Adelson

FingerSLAM is constructed with two constituent pose estimators: a multi-pass refined tactile-based pose estimator that captures movements from detailed local textures, and a single-pass vision-based pose estimator that predicts from a global view of the object.

3D Reconstruction Object Localization +1

Tac2Pose: Tactile Object Pose Estimation from the First Touch

no code implementations25 Apr 2022 Maria Bauza, Antonia Bronars, Alberto Rodriguez

This results in a perception model that localizes objects from the first real tactile observation.

Contrastive Learning Pose Estimation

Experience-Embedded Visual Foresight

no code implementations12 Nov 2019 Lin Yen-Chen, Maria Bauza, Phillip Isola

In this paper, we tackle the generalization problem via fast adaptation, where we train a prediction model to quickly adapt to the observed visual dynamics of a novel object.

Video Prediction

Tactile Mapping and Localization from High-Resolution Tactile Imprints

no code implementations24 Apr 2019 Maria Bauza, Oleguer Canal, Alberto Rodriguez

This work studies the problem of shape reconstruction and object localization using a vision-based tactile sensor, GelSlim.

Object Localization Vocal Bursts Intensity Prediction

Graph Element Networks: adaptive, structured computation and memory

2 code implementations18 Apr 2019 Ferran Alet, Adarsh K. Jeewajee, Maria Bauza, Alberto Rodriguez, Tomas Lozano-Perez, Leslie Pack Kaelbling

We explore the use of graph neural networks (GNNs) to model spatial processes in which there is no a priori graphical structure.

Combining Physical Simulators and Object-Based Networks for Control

no code implementations13 Apr 2019 Anurag Ajay, Maria Bauza, Jiajun Wu, Nima Fazeli, Joshua B. Tenenbaum, Alberto Rodriguez, Leslie P. Kaelbling

Physics engines play an important role in robot planning and control; however, many real-world control problems involve complex contact dynamics that cannot be characterized analytically.

Modular meta-learning in abstract graph networks for combinatorial generalization

1 code implementation19 Dec 2018 Ferran Alet, Maria Bauza, Alberto Rodriguez, Tomas Lozano-Perez, Leslie P. Kaelbling

Modular meta-learning is a new framework that generalizes to unseen datasets by combining a small set of neural modules in different ways.


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

no code implementations9 Aug 2018 Anurag Ajay, Jiajun Wu, Nima Fazeli, Maria Bauza, Leslie P. Kaelbling, Joshua B. Tenenbaum, Alberto Rodriguez

An efficient, generalizable physical simulator with universal uncertainty estimates has wide applications in robot state estimation, planning, and control.

Gaussian Processes

A Data-Efficient Approach to Precise and Controlled Pushing

no code implementations26 Jul 2018 Maria Bauza, Francois R. Hogan, Alberto Rodriguez

Decades of research in control theory have shown that simple controllers, when provided with timely feedback, can control complex systems.


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

no code implementations23 Sep 2017 Maria Bauza, Alberto Rodriguez

On the other hand, it achieves effective sampling and accurate probabilistic propagation by relying on the GP form of the system, and the sum-of-Gaussian form of the belief.

Gaussian Processes

A probabilistic data-driven model for planar pushing

no code implementations10 Apr 2017 Maria Bauza, Alberto Rodriguez

This paper presents a data-driven approach to model planar pushing interaction to predict both the most likely outcome of a push and its expected variability.

Gaussian Processes

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