Search Results for author: Frederik Ebert

Found 16 papers, 9 papers with code

Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials

1 code implementation11 Oct 2022 Aviral Kumar, Anikait Singh, Frederik Ebert, Mitsuhiko Nakamoto, Yanlai Yang, Chelsea Finn, Sergey Levine

To our knowledge, PTR is the first RL method that succeeds at learning new tasks in a new domain on a real WidowX robot with as few as 10 task demonstrations, by effectively leveraging an existing dataset of diverse multi-task robot data collected in a variety of toy kitchens.

Offline RL Q-Learning +1

BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning

no code implementations4 Feb 2022 Eric Jang, Alex Irpan, Mohi Khansari, Daniel Kappler, Frederik Ebert, Corey Lynch, Sergey Levine, Chelsea Finn

In this paper, we study the problem of enabling a vision-based robotic manipulation system to generalize to novel tasks, a long-standing challenge in robot learning.

Imitation Learning

Model-Based Visual Planning with Self-Supervised Functional Distances

1 code implementation ICLR 2021 Stephen Tian, Suraj Nair, Frederik Ebert, Sudeep Dasari, Benjamin Eysenbach, Chelsea Finn, Sergey Levine

In our experiments, we find that our method can successfully learn models that perform a variety of tasks at test-time, moving objects amid distractors with a simulated robotic arm and even learning to open and close a drawer using a real-world robot.

reinforcement-learning Reinforcement Learning (RL)

Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors

1 code implementation NeurIPS 2020 Karl Pertsch, Oleh Rybkin, Frederik Ebert, Chelsea Finn, Dinesh Jayaraman, Sergey Levine

In this work we propose a framework for visual prediction and planning that is able to overcome both of these limitations.

OmniTact: A Multi-Directional High Resolution Touch Sensor

1 code implementation16 Mar 2020 Akhil Padmanabha, Frederik Ebert, Stephen Tian, Roberto Calandra, Chelsea Finn, Sergey Levine

We compare with a state-of-the-art tactile sensor that is only sensitive on one side, as well as a state-of-the-art multi-directional tactile sensor, and find that OmniTact's combination of high-resolution and multi-directional sensing is crucial for reliably inserting the electrical connector and allows for higher accuracy in the state estimation task.

Vocal Bursts Intensity Prediction

RoboNet: Large-Scale Multi-Robot Learning

no code implementations24 Oct 2019 Sudeep Dasari, Frederik Ebert, Stephen Tian, Suraj Nair, Bernadette Bucher, Karl Schmeckpeper, Siddharth Singh, Sergey Levine, Chelsea Finn

This leads to a frequent tension in robotic learning: how can we learn generalizable robotic controllers without having to collect impractically large amounts of data for each separate experiment?

Test Video Prediction

Goal-Conditioned Video Prediction

no code implementations25 Sep 2019 Oleh Rybkin, Karl Pertsch, Frederik Ebert, Dinesh Jayaraman, Chelsea Finn, Sergey Levine

Prior work on video generation largely focuses on prediction models that only observe frames from the beginning of the video.

Imitation Learning Video Generation +1

Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresight

no code implementations11 Apr 2019 Annie Xie, Frederik Ebert, Sergey Levine, Chelsea Finn

Machine learning techniques have enabled robots to learn narrow, yet complex tasks and also perform broad, yet simple skills with a wide variety of objects.

Imitation Learning Self-Supervised Learning

Manipulation by Feel: Touch-Based Control with Deep Predictive Models

no code implementations11 Mar 2019 Stephen Tian, Frederik Ebert, Dinesh Jayaraman, Mayur Mudigonda, Chelsea Finn, Roberto Calandra, Sergey Levine

Touch sensing is widely acknowledged to be important for dexterous robotic manipulation, but exploiting tactile sensing for continuous, non-prehensile manipulation is challenging.

Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control

1 code implementation3 Dec 2018 Frederik Ebert, Chelsea Finn, Sudeep Dasari, Annie Xie, Alex Lee, Sergey Levine

Deep reinforcement learning (RL) algorithms can learn complex robotic skills from raw sensory inputs, but have yet to achieve the kind of broad generalization and applicability demonstrated by deep learning methods in supervised domains.

reinforcement-learning Reinforcement Learning (RL) +1

Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning

3 code implementations6 Oct 2018 Frederik Ebert, Sudeep Dasari, Alex X. Lee, Sergey Levine, Chelsea Finn

We demonstrate that this idea can be combined with a video-prediction based controller to enable complex behaviors to be learned from scratch using only raw visual inputs, including grasping, repositioning objects, and non-prehensile manipulation.

Image Registration Self-Supervised Learning +1

Stochastic Adversarial Video Prediction

4 code implementations ICLR 2019 Alex X. Lee, Richard Zhang, Frederik Ebert, Pieter Abbeel, Chelsea Finn, Sergey Levine

However, learning to predict raw future observations, such as frames in a video, is exceedingly challenging -- the ambiguous nature of the problem can cause a naively designed model to average together possible futures into a single, blurry prediction.

 Ranked #1 on Video Prediction on KTH (Cond metric)

Representation Learning Video Generation +1

Self-Supervised Learning of Object Motion Through Adversarial Video Prediction

no code implementations ICLR 2018 Alex X. Lee, Frederik Ebert, Richard Zhang, Chelsea Finn, Pieter Abbeel, Sergey Levine

In this paper, we study the problem of multi-step video prediction, where the goal is to predict a sequence of future frames conditioned on a short context.

Self-Supervised Learning Video Prediction

Self-Supervised Visual Planning with Temporal Skip Connections

3 code implementations15 Oct 2017 Frederik Ebert, Chelsea Finn, Alex X. Lee, Sergey Levine

One learning signal that is always available for autonomously collected data is prediction: if a robot can learn to predict the future, it can use this predictive model to take actions to produce desired outcomes, such as moving an object to a particular location.

Video Prediction

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