48 citations · 207 across the 14 of their papers we have counts for
20 papers
simPLE: a visuotactile method learned in simulation to precisely pick, localize, regrasp, and place objects
Maria Bauza, Antonia Bronars, Yifan Hou +3
Existing robotic systems have a clear tension between generality and precision. Deployed solutions for robotic manipulation tend to fall into the paradigm of one robot solving a si…
RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation
Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao +36
The ability to leverage heterogeneous robotic experience from different robots and tasks to quickly master novel skills and embodiments has the potential to transform robot learnin…
FingerSLAM: Closed-loop Unknown Object Localization and Reconstruction from Visuo-tactile Feedback
Jialiang Zhao, Maria Bauza, Edward H. Adelson
In this paper, we address the problem of using visuo-tactile feedback for 6-DoF localization and 3D reconstruction of unknown in-hand objects. We propose FingerSLAM, a closed-loop…
Tac2Pose: Tactile Object Pose Estimation from the First Touch
Maria Bauza, Antonia Bronars, Alberto Rodriguez
In this paper, we present Tac2Pose, an object-specific approach to tactile pose estimation from the first touch for known objects. Given the object geometry, we learn a tailored pe…
Tactile Object Pose Estimation from the First Touch with Geometric Contact Rendering
Maria Bauza, Eric Valls, Bryan Lim +2
In this paper, we present an approach to tactile pose estimation from the first touch for known objects. First, we create an object-agnostic map from real tactile observations to c…
Tactile SLAM: Real-time inference of shape and pose from planar pushing
Sudharshan Suresh, Maria Bauza, Kuan-Ting Yu +3
Tactile perception is central to robot manipulation in unstructured environments. However, it requires contact, and a mature implementation must infer object models while also acco…
Experience-Embedded Visual Foresight
Lin Yen-Chen, Maria Bauza, Phillip Isola
Visual foresight gives an agent a window into the future, which it can use to anticipate events before they happen and plan strategic behavior. Although impressive results have bee…
Accurate Vision-based Manipulation through Contact Reasoning
Alina Kloss, Maria Bauza, Jiajun Wu +3
Planning contact interactions is one of the core challenges of many robotic tasks. Optimizing contact locations while taking dynamics into account is computationally costly and, in…
Omnipush: accurate, diverse, real-world dataset of pushing dynamics with RGB-D video
Maria Bauza, Ferran Alet, Yen-Chen Lin +4
Pushing is a fundamental robotic skill. Existing work has shown how to exploit models of pushing to achieve a variety of tasks, including grasping under uncertainty, in-hand manipu…
Combining Physical Simulators and Object-Based Networks for Control
Anurag Ajay, Maria Bauza, Jiajun Wu +4
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 analyt…
Tactile Mapping and Localization from High-Resolution Tactile Imprints
Maria Bauza, Oleguer Canal, Alberto Rodriguez
This work studies the problem of shape reconstruction and object localization using a vision-based tactile sensor, GelSlim. The main contributions are the recovery of local shapes…
Graph Element Networks: adaptive, structured computation and memory
Ferran Alet, Adarsh K. Jeewajee, Maria Bauza +3
We explore the use of graph neural networks (GNNs) to model spatial processes in which there is no a priori graphical structure. Similar to finite element analysis, we assign nodes…
Modular meta-learning in abstract graph networks for combinatorial generalization
Ferran Alet, Maria Bauza, Alberto Rodriguez +2
Modular meta-learning is a new framework that generalizes to unseen datasets by combining a small set of neural modules in different ways. In this work we propose abstract graph ne…
Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing
Anurag Ajay, Jiajun Wu, Nima Fazeli +4
An efficient, generalizable physical simulator with universal uncertainty estimates has wide applications in robot state estimation, planning, and control. In this paper, we build…
A Data-Efficient Approach to Precise and Controlled Pushing
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. Pushing is an example of a complex mechan…
Tactile Regrasp: Grasp Adjustments via Simulated Tactile Transformations
Francois R. Hogan, Maria Bauza, Oleguer Canal +2
This paper presents a novel regrasp control policy that makes use of tactile sensing to plan local grasp adjustments. Our approach determines regrasp actions by virtually searching…
Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching
Andy Zeng, Shuran Song, Kuan-Ting Yu +18
This paper presents a robotic pick-and-place system that is capable of grasping and recognizing both known and novel objects in cluttered environments. The key new feature of the s…
GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs
Maria Bauza, Alberto Rodriguez
This work studies the problem of stochastic dynamic filtering and state propagation with complex beliefs. The main contribution is GP-SUM, a filtering algorithm tailored to dynamic…
A probabilistic data-driven model for planar pushing
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. The learned models re…
More than a Million Ways to Be Pushed: A High-Fidelity Experimental Dataset of Planar Pushing
Kuan-Ting Yu, Maria Bauza, Nima Fazeli +1
Pushing is a motion primitive useful to handle objects that are too large, too heavy, or too cluttered to be grasped. It is at the core of much of robotic manipulation, in particul…