Search Results for author: Jordi Salvador

Found 12 papers, 4 papers with code

Objaverse: A Universe of Annotated 3D Objects

no code implementations15 Dec 2022 Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, Ali Farhadi

Massive data corpora like WebText, Wikipedia, Conceptual Captions, WebImageText, and LAION have propelled recent dramatic progress in AI.

A General Purpose Supervisory Signal for Embodied Agents

no code implementations1 Dec 2022 Kunal Pratap Singh, Jordi Salvador, Luca Weihs, Aniruddha Kembhavi

Training effective embodied AI agents often involves manual reward engineering, expert imitation, specialized components such as maps, or leveraging additional sensors for depth and localization.

Contrastive Learning Representation Learning

ASC me to Do Anything: Multi-task Training for Embodied AI

no code implementations14 Feb 2022 Jiasen Lu, Jordi Salvador, Roozbeh Mottaghi, Aniruddha Kembhavi

We propose Atomic Skill Completion (ASC), an approach for multi-task training for Embodied AI, where a set of atomic skills shared across multiple tasks are composed together to perform the tasks.

Towards Disturbance-Free Visual Mobile Manipulation

1 code implementation17 Dec 2021 Tianwei Ni, Kiana Ehsani, Luca Weihs, Jordi Salvador

In this paper, we study the problem of training agents to complete the task of visual mobile manipulation in the ManipulaTHOR environment while avoiding unnecessary collision (disturbance) with objects.

Knowledge Distillation Visual Navigation

AllenAct: A Framework for Embodied AI Research

1 code implementation28 Aug 2020 Luca Weihs, Jordi Salvador, Klemen Kotar, Unnat Jain, Kuo-Hao Zeng, Roozbeh Mottaghi, Aniruddha Kembhavi

The domain of Embodied AI, in which agents learn to complete tasks through interaction with their environment from egocentric observations, has experienced substantial growth with the advent of deep reinforcement learning and increased interest from the computer vision, NLP, and robotics communities.

Embodied Question Answering Instruction Following +1

Bridging the Imitation Gap by Adaptive Insubordination

no code implementations NeurIPS 2021 Luca Weihs, Unnat Jain, Iou-Jen Liu, Jordi Salvador, Svetlana Lazebnik, Aniruddha Kembhavi, Alexander Schwing

However, we show that when the teaching agent makes decisions with access to privileged information that is unavailable to the student, this information is marginalized during imitation learning, resulting in an "imitation gap" and, potentially, poor results.

Imitation Learning Memorization +2

Learning About Objects by Learning to Interact with Them

no code implementations NeurIPS 2020 Martin Lohmann, Jordi Salvador, Aniruddha Kembhavi, Roozbeh Mottaghi

Much of the remarkable progress in computer vision has been focused around fully supervised learning mechanisms relying on highly curated datasets for a variety of tasks.

RoboTHOR: An Open Simulation-to-Real Embodied AI Platform

1 code implementation CVPR 2020 Matt Deitke, Winson Han, Alvaro Herrasti, Aniruddha Kembhavi, Eric Kolve, Roozbeh Mottaghi, Jordi Salvador, Dustin Schwenk, Eli VanderBilt, Matthew Wallingford, Luca Weihs, Mark Yatskar, Ali Farhadi

We argue that interactive and embodied visual AI has reached a stage of development similar to visual recognition prior to the advent of these ecosystems.

PSyCo: Manifold Span Reduction for Super Resolution

no code implementations CVPR 2016 Eduardo Perez-Pellitero, Jordi Salvador, Javier Ruiz-Hidalgo, Bodo Rosenhahn

The main challenge in Super Resolution (SR) is to discover the mapping between the low- and high-resolution manifolds of image patches, a complex ill-posed problem which has recently been addressed through piecewise linear regression with promising results.

regression Super-Resolution

Naive Bayes Super-Resolution Forest

no code implementations ICCV 2015 Jordi Salvador, Eduardo Perez-Pellitero

This paper presents a fast, high-performance method for super resolution with external learning.


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