Pierre Sermanet | DeepAI

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∙ 09/06/2023

Robotic Table Tennis: A Case Study into a High Speed Learning System

We present a deep-dive into a real-world robotic learning system that, i...

0 David B. D'Ambrosio, et al. ∙

∙ 07/28/2023

RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

We study how vision-language models trained on Internet-scale data can b...

0 Anthony Brohan, et al. ∙

∙ 03/06/2023

PaLM-E: An Embodied Multimodal Language Model

Large language models excel at a wide range of complex tasks. However, e...

0 Danny Driess, et al. ∙

∙ 11/21/2022

Robotic Skill Acquisition via Instruction Augmentation with Vision-Language Models

In recent years, much progress has been made in learning robotic manipul...

0 Ted Xiao, et al. ∙

∙ 10/07/2022

GoalsEye: Learning High Speed Precision Table Tennis on a Physical Robot

Learning goal conditioned control in the real world is a challenging ope...

0 Tianli Ding, et al. ∙

∙ 07/12/2022

Inner Monologue: Embodied Reasoning through Planning with Language Models

Recent works have shown how the reasoning capabilities of Large Language...

8 Wenlong Huang, et al. ∙

∙ 05/12/2022

Visuomotor Control in Multi-Object Scenes Using Object-Aware Representations

Perceptual understanding of the scene and the relationship between its d...

0 Negin Heravi, et al. ∙

∙ 04/04/2022

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

Large language models can encode a wealth of semantic knowledge about th...

2 Michael Ahn, et al. ∙

∙ 04/29/2021

With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations

Self-supervised learning algorithms based on instance discrimination tra...

4 Debidatta Dwibedi, et al. ∙

∙ 10/13/2020

Broadly-Exploring, Local-Policy Trees for Long-Horizon Task Planning

Long-horizon planning in realistic environments requires the ability to ...

0 Brian Ichter, et al. ∙

∙ 06/27/2020

Counting Out Time: Class Agnostic Video Repetition Counting in the Wild

We present an approach for estimating the period with which an action is...

4 Debidatta Dwibedi, et al. ∙

∙ 06/11/2020

Learning to Play by Imitating Humans

Acquiring multiple skills has commonly involved collecting a large numbe...

0 Rostam Dinyari, et al. ∙

∙ 05/31/2020

Motion2Vec: Semi-Supervised Representation Learning from Surgical Videos

Learning meaningful visual representations in an embedding space can fac...

21 Ajay Kumar Tanwani, et al. ∙

∙ 05/15/2020

Grounding Language in Play

Natural language is perhaps the most versatile and intuitive way for hum...

0 Corey Lynch, et al. ∙

∙ 06/10/2019

Online Object Representations with Contrastive Learning

We propose a self-supervised approach for learning representations of ob...

0 Sören Pirk, et al. ∙

∙ 04/16/2019

Temporal Cycle-Consistency Learning

We introduce a self-supervised representation learning method based on t...

2 Debidatta Dwibedi, et al. ∙

∙ 03/28/2019

Wasserstein Dependency Measure for Representation Learning

Mutual information maximization has emerged as a powerful learning objec...

18 Sherjil Ozair, et al. ∙

∙ 03/05/2019

Learning Latent Plans from Play

We propose learning from teleoperated play data (LfP) as a way to scale ...

0 Corey Lynch, et al. ∙

∙ 08/02/2018

Learning Actionable Representations from Visual Observations

In this work we explore a new approach for robots to teach themselves ab...

0 Debidatta Dwibedi, et al. ∙

∙ 12/20/2016

Unsupervised Perceptual Rewards for Imitation Learning

Reward function design and exploration time are arguably the biggest obs...

0 Pierre Sermanet, et al. ∙

∙ 12/22/2014

Attention for Fine-Grained Categorization

This paper presents experiments extending the work of Ba et al. (2014) o...

0 Pierre Sermanet, et al. ∙

∙ 09/17/2014

Going Deeper with Convolutions

We propose a deep convolutional neural network architecture codenamed "I...

0 Christian Szegedy, et al. ∙

∙ 12/21/2013

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

We present an integrated framework for using Convolutional Networks for ...

0 Pierre Sermanet, et al. ∙

∙ 12/01/2012

Pedestrian Detection with Unsupervised Multi-Stage Feature Learning

Pedestrian detection is a problem of considerable practical interest. Ad...

0 Pierre Sermanet, et al. ∙

∙ 04/18/2012

Convolutional Neural Networks Applied to House Numbers Digit Classification

We classify digits of real-world house numbers using convolutional neura...

0 Pierre Sermanet, et al. ∙

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