Search Results for author: Coline Devin

Found 20 papers, 6 papers with code

How to Spend Your Robot Time: Bridging Kickstarting and Offline Reinforcement Learning for Vision-based Robotic Manipulation

no code implementations6 May 2022 Alex X. Lee, Coline Devin, Jost Tobias Springenberg, Yuxiang Zhou, Thomas Lampe, Abbas Abdolmaleki, Konstantinos Bousmalis

Our analysis, both in simulation and in the real world, shows that our approach is the best across data budgets, while standard offline RL from teacher rollouts is surprisingly effective when enough data is given.

Offline RL

Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulation

no code implementations28 Jul 2021 Charles Sun, Jędrzej Orbik, Coline Devin, Brian Yang, Abhishek Gupta, Glen Berseth, Sergey Levine

Our aim is to devise a robotic reinforcement learning system for learning navigation and manipulation together, in an autonomous way without human intervention, enabling continual learning under realistic assumptions.

Continual Learning reinforcement-learning

Modular Networks for Compositional Instruction Following

no code implementations NAACL 2021 Rodolfo Corona, Daniel Fried, Coline Devin, Dan Klein, Trevor Darrell

In our approach, subgoal modules each carry out natural language instructions for a specific subgoal type.

Self-Supervised Goal-Conditioned Pick and Place

no code implementations26 Aug 2020 Coline Devin, Payam Rowghanian, Chris Vigorito, Will Richards, Khashayar Rohanimanesh

Robots have the capability to collect large amounts of data autonomously by interacting with objects in the world.

Robotics

Learning to Reach Goals via Iterated Supervised Learning

2 code implementations ICLR 2021 Dibya Ghosh, Abhishek Gupta, Ashwin Reddy, Justin Fu, Coline Devin, Benjamin Eysenbach, Sergey Levine

Current reinforcement learning (RL) algorithms can be brittle and difficult to use, especially when learning goal-reaching behaviors from sparse rewards.

Multi-Goal Reinforcement Learning

Compositional Plan Vectors

1 code implementation NeurIPS 2019 Coline Devin, Daniel Geng, Pieter Abbeel, Trevor Darrell, Sergey Levine

We show that CPVs can be learned within a one-shot imitation learning framework without any additional supervision or information about task hierarchy, and enable a demonstration-conditioned policy to generalize to tasks that sequence twice as many skills as the tasks seen during training.

Imitation Learning

Plan Arithmetic: Compositional Plan Vectors for Multi-Task Control

no code implementations30 Oct 2019 Coline Devin, Daniel Geng, Pieter Abbeel, Trevor Darrell, Sergey Levine

We show that CPVs can be learned within a one-shot imitation learning framework without any additional supervision or information about task hierarchy, and enable a demonstration-conditioned policy to generalize to tasks that sequence twice as many skills as the tasks seen during training.

Imitation Learning

Learning to Reach Goals Without Reinforcement Learning

no code implementations25 Sep 2019 Dibya Ghosh, Abhishek Gupta, Justin Fu, Ashwin Reddy, Coline Devin, Benjamin Eysenbach, Sergey Levine

By maximizing the likelihood of good actions provided by an expert demonstrator, supervised imitation learning can produce effective policies without the algorithmic complexities and optimization challenges of reinforcement learning, at the cost of requiring an expert demonstrator -- typically a person -- to provide the demonstrations.

Imitation Learning reinforcement-learning

Monocular Plan View Networks for Autonomous Driving

no code implementations16 May 2019 Dequan Wang, Coline Devin, Qi-Zhi Cai, Philipp Krähenbühl, Trevor Darrell

Convolutions on monocular dash cam videos capture spatial invariances in the image plane but do not explicitly reason about distances and depth.

3D Object Detection Autonomous Driving

Grasp2Vec: Learning Object Representations from Self-Supervised Grasping

1 code implementation16 Nov 2018 Eric Jang, Coline Devin, Vincent Vanhoucke, Sergey Levine

We formulate an arithmetic relationship between feature vectors from this observation, and use it to learn a representation of scenes and objects that can then be used to identify object instances, localize them in the scene, and perform goal-directed grasping tasks where the robot must retrieve commanded objects from a bin.

Representation Learning

Deep Object-Centric Policies for Autonomous Driving

no code implementations13 Nov 2018 Dequan Wang, Coline Devin, Qi-Zhi Cai, Fisher Yu, Trevor Darrell

While learning visuomotor skills in an end-to-end manner is appealing, deep neural networks are often uninterpretable and fail in surprising ways.

Autonomous Driving

Deep Object-Centric Representations for Generalizable Robot Learning

1 code implementation14 Aug 2017 Coline Devin, Pieter Abbeel, Trevor Darrell, Sergey Levine

We devise an object-level attentional mechanism that can be used to determine relevant objects from a few trajectories or demonstrations, and then immediately incorporate those objects into a learned policy.

Learning Modular Neural Network Policies for Multi-Task and Multi-Robot Transfer

no code implementations22 Sep 2016 Coline Devin, Abhishek Gupta, Trevor Darrell, Pieter Abbeel, Sergey Levine

Using deep reinforcement learning to train general purpose neural network policies alleviates some of the burden of manual representation engineering by using expressive policy classes, but exacerbates the challenge of data collection, since such methods tend to be less efficient than RL with low-dimensional, hand-designed representations.

reinforcement-learning Transfer Learning

Adapting Deep Visuomotor Representations with Weak Pairwise Constraints

no code implementations23 Nov 2015 Eric Tzeng, Coline Devin, Judy Hoffman, Chelsea Finn, Pieter Abbeel, Sergey Levine, Kate Saenko, Trevor Darrell

We propose a novel, more powerful combination of both distribution and pairwise image alignment, and remove the requirement for expensive annotation by using weakly aligned pairs of images in the source and target domains.

Domain Adaptation

Embedding Word Similarity with Neural Machine Translation

no code implementations19 Dec 2014 Felix Hill, Kyunghyun Cho, Sebastien Jean, Coline Devin, Yoshua Bengio

Here we investigate the embeddings learned by neural machine translation models, a recently-developed class of neural language model.

Language Modelling Machine Translation +2

Not All Neural Embeddings are Born Equal

no code implementations2 Oct 2014 Felix Hill, Kyunghyun Cho, Sebastien Jean, Coline Devin, Yoshua Bengio

Neural language models learn word representations that capture rich linguistic and conceptual information.

Machine Translation Translation

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