no code implementations • 20 Jun 2023 • Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao, Coline Devin, Alex X. Lee, Maria Bauza, Todor Davchev, Yuxiang Zhou, Agrim Gupta, Akhil Raju, Antoine Laurens, Claudio Fantacci, Valentin Dalibard, Martina Zambelli, Murilo Martins, Rugile Pevceviciute, Michiel Blokzijl, Misha Denil, Nathan Batchelor, Thomas Lampe, Emilio Parisotto, Konrad Żołna, Scott Reed, Sergio Gómez Colmenarejo, Jon Scholz, Abbas Abdolmaleki, Oliver Groth, Jean-Baptiste Regli, Oleg Sushkov, Tom Rothörl, José Enrique Chen, Yusuf Aytar, Dave Barker, Joy Ortiz, Martin Riedmiller, Jost Tobias Springenberg, Raia Hadsell, Francesco Nori, Nicolas Heess
With RoboCat, we demonstrate the ability to generalise to new tasks and robots, both zero-shot as well as through adaptation using only 100-1000 examples for the target task.
no code implementations • 6 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.
1 code implementation • 12 Oct 2021 • Alex X. Lee, Coline Devin, Yuxiang Zhou, Thomas Lampe, Konstantinos Bousmalis, Jost Tobias Springenberg, Arunkumar Byravan, Abbas Abdolmaleki, Nimrod Gileadi, David Khosid, Claudio Fantacci, Jose Enrique Chen, Akhil Raju, Rae Jeong, Michael Neunert, Antoine Laurens, Stefano Saliceti, Federico Casarini, Martin Riedmiller, Raia Hadsell, Francesco Nori
We study the problem of robotic stacking with objects of complex geometry.
Ranked #2 on
Skill Generalization
on RGB-Stacking
no code implementations • 28 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.
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.
no code implementations • 26 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
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.
1 code implementation • ICLR 2021 • Glen Berseth, Daniel Geng, Coline Devin, Nicholas Rhinehart, Chelsea Finn, Dinesh Jayaraman, Sergey Levine
Every living organism struggles against disruptive environmental forces to carve out and maintain an orderly niche.
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.
no code implementations • 30 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.
no code implementations • 25 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.
no code implementations • 25 Sep 2019 • Glen Berseth, Daniel Geng, Coline Devin, Dinesh Jayaraman, Chelsea Finn, Sergey Levine
All living organisms struggle against the forces of nature to carve out niches where they can maintain relative stasis.
Unsupervised Pre-training
Unsupervised Reinforcement Learning
no code implementations • 16 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.
1 code implementation • 16 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.
no code implementations • 13 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.
1 code implementation • 14 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.
no code implementations • 8 Mar 2017 • Abhishek Gupta, Coline Devin, Yuxuan Liu, Pieter Abbeel, Sergey Levine
People can learn a wide range of tasks from their own experience, but can also learn from observing other creatures.
no code implementations • 22 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.
no code implementations • 23 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.
no code implementations • 19 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.
no code implementations • 2 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.