Search Results for author: Pierre Sermanet

Found 19 papers, 6 papers with code

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

no code implementations13 Oct 2020 Brian Ichter, Pierre Sermanet, Corey Lynch

This task space can be quite general and abstract; its only requirements are to be sampleable and to well-cover the space of useful tasks.

Motion Planning

Learning to Play by Imitating Humans

no code implementations11 Jun 2020 Rostam Dinyari, Pierre Sermanet, Corey Lynch

Acquiring multiple skills has commonly involved collecting a large number of expert demonstrations per task or engineering custom reward functions.

Motion2Vec: Semi-Supervised Representation Learning from Surgical Videos

no code implementations31 May 2020 Ajay Kumar Tanwani, Pierre Sermanet, Andy Yan, Raghav Anand, Mariano Phielipp, Ken Goldberg

We demonstrate the use of this representation to imitate surgical suturing motions from publicly available videos of the JIGSAWS dataset.

Action Segmentation Metric Learning +1

Language Conditioned Imitation Learning over Unstructured Data

no code implementations15 May 2020 Corey Lynch, Pierre Sermanet

Prior work in imitation learning typically requires each task be specified with a task id or goal image -- something that is often impractical in open-world environments.

Continuous Control Imitation Learning +1

Online Object Representations with Contrastive Learning

no code implementations10 Jun 2019 Sören Pirk, Mohi Khansari, Yunfei Bai, Corey Lynch, Pierre Sermanet

We propose a self-supervised approach for learning representations of objects from monocular videos and demonstrate it is particularly useful in situated settings such as robotics.

Contrastive Learning

Wasserstein Dependency Measure for Representation Learning

no code implementations NeurIPS 2019 Sherjil Ozair, Corey Lynch, Yoshua Bengio, Aaron van den Oord, Sergey Levine, Pierre Sermanet

Mutual information maximization has emerged as a powerful learning objective for unsupervised representation learning obtaining state-of-the-art performance in applications such as object recognition, speech recognition, and reinforcement learning.

Object Recognition Speech Recognition +2

Learning Latent Plans from Play

no code implementations5 Mar 2019 Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, Pierre Sermanet

Learning from play (LfP) offers three main advantages: 1) It is cheap.

Robotics

Learning Actionable Representations from Visual Observations

no code implementations2 Aug 2018 Debidatta Dwibedi, Jonathan Tompson, Corey Lynch, Pierre Sermanet

In this work we explore a new approach for robots to teach themselves about the world simply by observing it.

Continuous Control

Time-Contrastive Networks: Self-Supervised Learning from Video

4 code implementations23 Apr 2017 Pierre Sermanet, Corey Lynch, Yevgen Chebotar, Jasmine Hsu, Eric Jang, Stefan Schaal, Sergey Levine

While representations are learned from an unlabeled collection of task-related videos, robot behaviors such as pouring are learned by watching a single 3rd-person demonstration by a human.

Metric Learning Self-Supervised Learning +1

Unsupervised Perceptual Rewards for Imitation Learning

no code implementations20 Dec 2016 Pierre Sermanet, Kelvin Xu, Sergey Levine

We present a method that is able to identify key intermediate steps of a task from only a handful of demonstration sequences, and automatically identify the most discriminative features for identifying these steps.

Imitation Learning

Attention for Fine-Grained Categorization

no code implementations22 Dec 2014 Pierre Sermanet, Andrea Frome, Esteban Real

This paper presents experiments extending the work of Ba et al. (2014) on recurrent neural models for attention into less constrained visual environments, specifically fine-grained categorization on the Stanford Dogs data set.

Going Deeper with Convolutions

65 code implementations CVPR 2015 Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich

We propose a deep convolutional neural network architecture codenamed "Inception", which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014).

Classification General Classification +3

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

5 code implementations21 Dec 2013 Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus, Yann Lecun

This integrated framework is the winner of the localization task of the ImageNet Large Scale Visual Recognition Challenge 2013 (ILSVRC2013) and obtained very competitive results for the detection and classifications tasks.

General Classification Image Classification +2

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