Search Results for author: Alberto Rodriguez

Found 27 papers, 9 papers with code

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

NeRF-Supervision: Learning Dense Object Descriptors from Neural Radiance Fields

no code implementations3 Mar 2022 Lin Yen-Chen, Pete Florence, Jonathan T. Barron, Tsung-Yi Lin, Alberto Rodriguez, Phillip Isola

In particular, we demonstrate that a NeRF representation of a scene can be used to train dense object descriptors.

Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation

no code implementations9 Dec 2021 Anthony Simeonov, Yilun Du, Andrea Tagliasacchi, Joshua B. Tenenbaum, Alberto Rodriguez, Pulkit Agrawal, Vincent Sitzmann

Our performance generalizes across both object instances and 6-DoF object poses, and significantly outperforms a recent baseline that relies on 2D descriptors.

A Differentiable Recipe for Learning Visual Non-Prehensile Planar Manipulation

1 code implementation9 Nov 2021 Bernardo Aceituno, Alberto Rodriguez, Shubham Tulsiani, Abhinav Gupta, Mustafa Mukadam

Specifying tasks with videos is a powerful technique towards acquiring novel and general robot skills.

Planning for Multi-stage Forceful Manipulation

no code implementations7 Jan 2021 Rachel Holladay, Tomás Lozano-Pérez, Alberto Rodriguez

The robot must choose a sequence of discrete actions, or strategy, such as whether to pick up an object, and the continuous parameters of each of those actions, such as how to grasp the object.

Robotics

Robotic Grasping of Fully-Occluded Objects using RF Perception

no code implementations31 Dec 2020 Tara Boroushaki, Junshan Leng, Ian Clester, Alberto Rodriguez, Fadel Adib

We present the design, implementation, and evaluation of RF-Grasp, a robotic system that can grasp fully-occluded objects in unknown and unstructured environments.

Efficient Exploration Robotic Grasping

INeRF: Inverting Neural Radiance Fields for Pose Estimation

1 code implementation10 Dec 2020 Lin Yen-Chen, Pete Florence, Jonathan T. Barron, Alberto Rodriguez, Phillip Isola, Tsung-Yi Lin

We then show that for complex real-world scenes from the LLFF dataset, iNeRF can improve NeRF by estimating the camera poses of novel images and using these images as additional training data for NeRF.

Pose Estimation

A Long Horizon Planning Framework for Manipulating Rigid Pointcloud Objects

no code implementations16 Nov 2020 Anthony Simeonov, Yilun Du, Beomjoon Kim, Francois R. Hogan, Joshua Tenenbaum, Pulkit Agrawal, Alberto Rodriguez

We present a framework for solving long-horizon planning problems involving manipulation of rigid objects that operates directly from a point-cloud observation, i. e. without prior object models.

Graph Attention Motion Planning

Tactile Dexterity: Manipulation Primitives with Tactile Feedback

no code implementations8 Feb 2020 Francois R. Hogan, Jose Ballester, Siyuan Dong, Alberto Rodriguez

This paper develops closed-loop tactile controllers for dexterous manipulation with dual-arm robotic palms.

Robotics Systems and Control Systems and Control

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

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.

TossingBot: Learning to Throw Arbitrary Objects with Residual Physics

no code implementations27 Mar 2019 Andy Zeng, Shuran Song, Johnny Lee, Alberto Rodriguez, Thomas Funkhouser

In this work, we propose an end-to-end formulation that jointly learns to infer control parameters for grasping and throwing motion primitives from visual observations (images of arbitrary objects in a bin) through trial and error.

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.

Meta-Learning

Maintaining Grasps within Slipping Bound by Monitoring Incipient Slip

1 code implementation31 Oct 2018 Siyuan Dong, Daolin Ma, Elliott Donlon, Alberto Rodriguez

The output is a dense slip field which we use to detect when small areas of the contact patch start to slip (incipient slip).

Robotics

A Convex-Combinatorial Model for Planar Caging

1 code implementation17 Sep 2018 Bernardo Aceituno-Cabezas, Hongkai Dai, Alberto Rodriguez

Caging is a promising tool which allows a robot to manipulate an object without directly reasoning about the contact dynamics involved.

Robotics

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.

Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning

4 code implementations27 Mar 2018 Andy Zeng, Shuran Song, Stefan Welker, Johnny Lee, Alberto Rodriguez, Thomas Funkhouser

Skilled robotic manipulation benefits from complex synergies between non-prehensile (e. g. pushing) and prehensile (e. g. grasping) actions: pushing can help rearrange cluttered objects to make space for arms and fingers; likewise, grasping can help displace objects to make pushing movements more precise and collision-free.

Q-Learning reinforcement-learning

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

Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge

2 code implementations29 Sep 2016 Andy Zeng, Kuan-Ting Yu, Shuran Song, Daniel Suo, Ed Walker Jr., Alberto Rodriguez, Jianxiong Xiao

The approach was part of the MIT-Princeton Team system that took 3rd- and 4th- place in the stowing and picking tasks, respectively at APC 2016.

6D Pose Estimation 6D Pose Estimation using RGBD

Analysis and Observations from the First Amazon Picking Challenge

no code implementations21 Jan 2016 Nikolaus Correll, Kostas E. Bekris, Dmitry Berenson, Oliver Brock, Albert Causo, Kris Hauser, Kei Okada, Alberto Rodriguez, Joseph M. Romano, Peter R. Wurman

This paper presents a overview of the inaugural Amazon Picking Challenge along with a summary of a survey conducted among the 26 participating teams.

Robotics

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