Search Results for author: Chelsea Finn

Found 145 papers, 59 papers with code

Improving Out-of-Distribution Robustness via Selective Augmentation

no code implementations2 Jan 2022 Huaxiu Yao, Yu Wang, Sai Li, Linjun Zhang, Weixin Liang, James Zou, Chelsea Finn

Machine learning algorithms typically assume that training and test examples are drawn from the same distribution.

Autonomous Reinforcement Learning: Formalism and Benchmarking

no code implementations17 Dec 2021 Archit Sharma, Kelvin Xu, Nikhil Sardana, Abhishek Gupta, Karol Hausman, Sergey Levine, Chelsea Finn

In this paper, we aim to address this discrepancy by laying out a framework for Autonomous Reinforcement Learning (ARL): reinforcement learning where the agent not only learns through its own experience, but also contends with lack of human supervision to reset between trials.

Extending the WILDS Benchmark for Unsupervised Adaptation

no code implementations9 Dec 2021 Shiori Sagawa, Pang Wei Koh, Tony Lee, Irena Gao, Sang Michael Xie, Kendrick Shen, Ananya Kumar, Weihua Hu, Michihiro Yasunaga, Henrik Marklund, Sara Beery, Etienne David, Ian Stavness, Wei Guo, Jure Leskovec, Kate Saenko, Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang

In this work, we present the WILDS 2. 0 update, which extends 8 of the 10 datasets in the WILDS benchmark of distribution shifts to include curated unlabeled data that would be realistically obtainable in deployment.

CoMPS: Continual Meta Policy Search

no code implementations8 Dec 2021 Glen Berseth, Zhiwei Zhang, Grace Zhang, Chelsea Finn, Sergey Levine

Beyond simply transferring past experience to new tasks, our goal is to devise continual reinforcement learning algorithms that learn to learn, using their experience on previous tasks to learn new tasks more quickly.

Continual Learning Continuous Control +3

Information is Power: Intrinsic Control via Information Capture

no code implementations NeurIPS 2021 Nicholas Rhinehart, Jenny Wang, Glen Berseth, John D. Co-Reyes, Danijar Hafner, Chelsea Finn, Sergey Levine

We study this question in dynamic partially-observed environments, and argue that a compact and general learning objective is to minimize the entropy of the agent's state visitation estimated using a latent state-space model.

Noether Networks: Meta-Learning Useful Conserved Quantities

no code implementations NeurIPS 2021 Ferran Alet, Dylan Doblar, Allan Zhou, Joshua Tenenbaum, Kenji Kawaguchi, Chelsea Finn

Progress in machine learning (ML) stems from a combination of data availability, computational resources, and an appropriate encoding of inductive biases.

Meta-Learning Translation

Meta-learning with an Adaptive Task Scheduler

1 code implementation NeurIPS 2021 Huaxiu Yao, Yu Wang, Ying WEI, Peilin Zhao, Mehrdad Mahdavi, Defu Lian, Chelsea Finn

In ATS, for the first time, we design a neural scheduler to decide which meta-training tasks to use next by predicting the probability being sampled for each candidate task, and train the scheduler to optimize the generalization capacity of the meta-model to unseen tasks.

Drug Discovery Meta-Learning

Fast Model Editing at Scale

1 code implementation21 Oct 2021 Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, Christopher D. Manning

To enable easy post-hoc editing at scale, we propose Model Editor Networks with Gradient Decomposition (MEND), a collection of small auxiliary editing networks that use a single desired input-output pair to make fast, local edits to a pre-trained model.

Language Modelling

MEMO: Test Time Robustness via Adaptation and Augmentation

1 code implementation18 Oct 2021 Marvin Zhang, Sergey Levine, Chelsea Finn

We study the problem of test time robustification, i. e., using the test input to improve model robustness.

A Workflow for Offline Model-Free Robotic Reinforcement Learning

no code implementations22 Sep 2021 Aviral Kumar, Anikait Singh, Stephen Tian, Chelsea Finn, Sergey Levine

To this end, we devise a set of metrics and conditions that can be tracked over the course of offline training, and can inform the practitioner about how the algorithm and model architecture should be adjusted to improve final performance.

Offline RL

Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks

no code implementations21 Sep 2021 Bohan Wu, Suraj Nair, Li Fei-Fei, Chelsea Finn

In this paper, we study the problem of learning a repertoire of low-level skills from raw images that can be sequenced to complete long-horizon visuomotor tasks.

Model-based Reinforcement Learning

Lifelong Robotic Reinforcement Learning by Retaining Experiences

no code implementations19 Sep 2021 Annie Xie, Chelsea Finn

Multi-task learning ideally allows robots to acquire a diverse repertoire of useful skills.

Multi-Task Learning

Conservative Data Sharing for Multi-Task Offline Reinforcement Learning

no code implementations NeurIPS 2021 Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Sergey Levine, Chelsea Finn

We argue that a natural use case of offline RL is in settings where we can pool large amounts of data collected in various scenarios for solving different tasks, and utilize all of this data to learn behaviors for all the tasks more effectively rather than training each one in isolation.

Offline RL

Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation

no code implementations2 Sep 2021 Suraj Nair, Eric Mitchell, Kevin Chen, Brian Ichter, Silvio Savarese, Chelsea Finn

However, goal images also have a number of drawbacks: they are inconvenient for humans to provide, they can over-specify the desired behavior leading to a sparse reward signal, or under-specify task information in the case of non-goal reaching tasks.

On the Opportunities and Risks of Foundation Models

1 code implementation16 Aug 2021 Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Kohd, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, aditi raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, Percy Liang

AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.

Transfer Learning

Bayesian Embeddings for Few-Shot Open World Recognition

no code implementations29 Jul 2021 John Willes, James Harrison, Ali Harakeh, Chelsea Finn, Marco Pavone, Steven Waslander

As autonomous decision-making agents move from narrow operating environments to unstructured worlds, learning systems must move from a closed-world formulation to an open-world and few-shot setting in which agents continuously learn new classes from small amounts of information.

Decision Making Few-Shot Learning

Autonomous Reinforcement Learning via Subgoal Curricula

no code implementations NeurIPS 2021 Archit Sharma, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn

Reinforcement learning (RL) promises to enable autonomous acquisition of complex behaviors for diverse agents.

ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback

1 code implementation23 Jul 2021 Mike Wu, Noah Goodman, Chris Piech, Chelsea Finn

High-quality computer science education is limited by the difficulty of providing instructor feedback to students at scale.

Few-Shot Learning

Differentiable Annealed Importance Sampling and the Perils of Gradient Noise

no code implementations NeurIPS 2021 Guodong Zhang, Kyle Hsu, Jianing Li, Chelsea Finn, Roger Grosse

To this end, we propose Differentiable AIS (DAIS), a variant of AIS which ensures differentiability by abandoning the Metropolis-Hastings corrections.

Stochastic Optimization

Just Train Twice: Improving Group Robustness without Training Group Information

1 code implementation19 Jul 2021 Evan Zheran Liu, Behzad Haghgoo, Annie S. Chen, aditi raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, Chelsea Finn

Standard training via empirical risk minimization (ERM) can produce models that achieve high accuracy on average but low accuracy on certain groups, especially in the presence of spurious correlations between the input and label.

Image Classification

Visual Adversarial Imitation Learning using Variational Models

no code implementations NeurIPS 2021 Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn

We consider a setting where an agent is provided a fixed dataset of visual demonstrations illustrating how to perform a task, and must learn to solve the task using the provided demonstrations and unsupervised environment interactions.

Imitation Learning Representation Learning

FitVid: Overfitting in Pixel-Level Video Prediction

1 code implementation24 Jun 2021 Mohammad Babaeizadeh, Mohammad Taghi Saffar, Suraj Nair, Sergey Levine, Chelsea Finn, Dumitru Erhan

There is a growing body of evidence that underfitting on the training data is one of the primary causes for the low quality predictions.

Image Augmentation Video Prediction

Intrinsic Control of Variational Beliefs in Dynamic Partially-Observed Visual Environments

no code implementations ICML Workshop URL 2021 Nicholas Rhinehart, Jenny Wang, Glen Berseth, John D Co-Reyes, Danijar Hafner, Chelsea Finn, Sergey Levine

We study this question in dynamic partially-observed environments, and argue that a compact and general learning objective is to minimize the entropy of the agent's state visitation estimated using a latent state-space model.

Meta-Learning with Fewer Tasks through Task Interpolation

no code implementations4 Jun 2021 Huaxiu Yao, Linjun Zhang, Chelsea Finn

Meta-learning enables algorithms to quickly learn a newly encountered task with just a few labeled examples by transferring previously learned knowledge.

Image Classification Meta-Learning +1

MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale

no code implementations16 Apr 2021 Dmitry Kalashnikov, Jacob Varley, Yevgen Chebotar, Benjamin Swanson, Rico Jonschkowski, Chelsea Finn, Sergey Levine, Karol Hausman

In this paper, we study how a large-scale collective robotic learning system can acquire a repertoire of behaviors simultaneously, sharing exploration, experience, and representations across tasks.

Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills

no code implementations15 Apr 2021 Yevgen Chebotar, Karol Hausman, Yao Lu, Ted Xiao, Dmitry Kalashnikov, Jake Varley, Alex Irpan, Benjamin Eysenbach, Ryan Julian, Chelsea Finn, Sergey Levine

We consider the problem of learning useful robotic skills from previously collected offline data without access to manually specified rewards or additional online exploration, a setting that is becoming increasingly important for scaling robot learning by reusing past robotic data.

Q-Learning

Learning Generalizable Robotic Reward Functions from "In-The-Wild" Human Videos

no code implementations31 Mar 2021 Annie S. Chen, Suraj Nair, Chelsea Finn

We find that by leveraging diverse human datasets, this reward function (a) can generalize zero shot to unseen environments, (b) generalize zero shot to unseen tasks, and (c) can be combined with visual model predictive control to solve robotic manipulation tasks on a real WidowX200 robot in an unseen environment from a single human demo.

Discriminator Augmented Model-Based Reinforcement Learning

no code implementations24 Mar 2021 Behzad Haghgoo, Allan Zhou, Archit Sharma, Chelsea Finn

By planning through a learned dynamics model, model-based reinforcement learning (MBRL) offers the prospect of good performance with little environment interaction.

Model-based Reinforcement Learning

FEW-SHOTLEARNING WITH WEAK SUPERVISION

no code implementations ICLR Workshop Learning_to_Learn 2021 Ali Ghadirzadeh, Petra Poklukar, Xi Chen, Huaxiu Yao, Hossein Azizpour, Mårten Björkman, Chelsea Finn, Danica Kragic

Few-shot meta-learning methods aim to learn the common structure shared across a set of tasks to facilitate learning new tasks with small amounts of data.

Meta-Learning Variational Inference

Greedy Hierarchical Variational Autoencoders for Large-Scale Video Prediction

no code implementations CVPR 2021 Bohan Wu, Suraj Nair, Roberto Martin-Martin, Li Fei-Fei, Chelsea Finn

Our key insight is that greedy and modular optimization of hierarchical autoencoders can simultaneously address both the memory constraints and the optimization challenges of large-scale video prediction.

Video Prediction

Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms

no code implementations5 Mar 2021 Ali Ghadirzadeh, Xi Chen, Petra Poklukar, Chelsea Finn, Mårten Björkman, Danica Kragic

Our results show that the proposed method can successfully adapt a trained policy to different robotic platforms with novel physical parameters and the superiority of our meta-learning algorithm compared to state-of-the-art methods for the introduced few-shot policy adaptation problem.

Meta-Learning

COMBO: Conservative Offline Model-Based Policy Optimization

2 code implementations NeurIPS 2021 Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn

We overcome this limitation by developing a new model-based offline RL algorithm, COMBO, that regularizes the value function on out-of-support state-action tuples generated via rollouts under the learned model.

Offline RL

How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned

no code implementations4 Feb 2021 Julian Ibarz, Jie Tan, Chelsea Finn, Mrinal Kalakrishnan, Peter Pastor, Sergey Levine

Learning to perceive and move in the real world presents numerous challenges, some of which are easier to address than others, and some of which are often not considered in RL research that focuses only on simulated domains.

Variable-Shot Adaptation for Incremental Meta-Learning

no code implementations1 Jan 2021 Tianhe Yu, Xinyang Geng, Chelsea Finn, Sergey Levine

Few-shot meta-learning methods consider the problem of learning new tasks from a small, fixed number of examples, by meta-learning across static data from a set of previous tasks.

Meta-Learning Zero-Shot Learning

Information Transfer in Multi-Task Learning

no code implementations1 Jan 2021 Chris Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn

Multi-task learning can leverage information learned by one task to benefit the training of other tasks.

Multi-Task Learning

On Trade-offs of Image Prediction in Visual Model-Based Reinforcement Learning

no code implementations1 Jan 2021 Mohammad Babaeizadeh, Mohammad Taghi Saffar, Danijar Hafner, Dumitru Erhan, Harini Kannan, Chelsea Finn, Sergey Levine

In this paper, we study a number of design decisions for the predictive model in visual MBRL algorithms, focusing specifically on methods that use a predictive model for planning.

Model-based Reinforcement 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.

Offline Reinforcement Learning from Images with Latent Space Models

no code implementations21 Dec 2020 Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn

In this work, we build on recent advances in model-based algorithms for offline RL, and extend them to high-dimensional visual observation spaces.

Offline RL

Variable-Shot Adaptation for Online Meta-Learning

no code implementations14 Dec 2020 Tianhe Yu, Xinyang Geng, Chelsea Finn, Sergey Levine

Few-shot meta-learning methods consider the problem of learning new tasks from a small, fixed number of examples, by meta-learning across static data from a set of previous tasks.

Meta-Learning Zero-Shot Learning

Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning

1 code implementation8 Dec 2020 Mohammad Babaeizadeh, Mohammad Taghi Saffar, Danijar Hafner, Harini Kannan, Chelsea Finn, Sergey Levine, Dumitru Erhan

In this paper, we study a number of design decisions for the predictive model in visual MBRL algorithms, focusing specifically on methods that use a predictive model for planning.

Model-based Reinforcement Learning

Continual Learning of Control Primitives : Skill Discovery via Reset-Games

no code implementations NeurIPS 2020 Kelvin Xu, Siddharth Verma, Chelsea Finn, Sergey Levine

First, in real world settings, when an agent attempts a tasks and fails, the environment must somehow "reset" so that the agent can attempt the task again.

Continual Learning

Reinforcement Learning with Videos: Combining Offline Observations with Interaction

1 code implementation12 Nov 2020 Karl Schmeckpeper, Oleh Rybkin, Kostas Daniilidis, Sergey Levine, Chelsea Finn

In this paper, we consider the question: can we perform reinforcement learning directly on experience collected by humans?

Learning Latent Representations to Influence Multi-Agent Interaction

no code implementations12 Nov 2020 Annie Xie, Dylan P. Losey, Ryan Tolsma, Chelsea Finn, Dorsa Sadigh

We propose a reinforcement learning-based framework for learning latent representations of an agent's policy, where the ego agent identifies the relationship between its behavior and the other agent's future strategy.

Continual Learning of Control Primitives: Skill Discovery via Reset-Games

1 code implementation10 Nov 2020 Kelvin Xu, Siddharth Verma, Chelsea Finn, Sergey Levine

Reinforcement learning has the potential to automate the acquisition of behavior in complex settings, but in order for it to be successfully deployed, a number of practical challenges must be addressed.

Continual Learning

Measuring and Harnessing Transference in Multi-Task Learning

no code implementations29 Oct 2020 Christopher Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn

Multi-task learning can leverage information learned by one task to benefit the training of other tasks.

Multi-Task Learning

Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones

1 code implementation29 Oct 2020 Brijen Thananjeyan, Ashwin Balakrishna, Suraj Nair, Michael Luo, Krishnan Srinivasan, Minho Hwang, Joseph E. Gonzalez, Julian Ibarz, Chelsea Finn, Ken Goldberg

Safety remains a central obstacle preventing widespread use of RL in the real world: learning new tasks in uncertain environments requires extensive exploration, but safety requires limiting exploration.

Safe Reinforcement Learning

One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL

no code implementations NeurIPS 2020 Saurabh Kumar, Aviral Kumar, Sergey Levine, Chelsea Finn

While reinforcement learning algorithms can learn effective policies for complex tasks, these policies are often brittle to even minor task variations, especially when variations are not explicitly provided during training.

Learning to be Safe: Deep RL with a Safety Critic

no code implementations27 Oct 2020 Krishnan Srinivasan, Benjamin Eysenbach, Sehoon Ha, Jie Tan, Chelsea Finn

Safety is an essential component for deploying reinforcement learning (RL) algorithms in real-world scenarios, and is critical during the learning process itself.

Transfer Learning

MELD: Meta-Reinforcement Learning from Images via Latent State Models

1 code implementation26 Oct 2020 Tony Z. Zhao, Anusha Nagabandi, Kate Rakelly, Chelsea Finn, Sergey Levine

Meta-reinforcement learning algorithms can enable autonomous agents, such as robots, to quickly acquire new behaviors by leveraging prior experience in a set of related training tasks.

Meta-Learning Meta Reinforcement Learning +1

Batch Exploration with Examples for Scalable Robotic Reinforcement Learning

1 code implementation22 Oct 2020 Annie S. Chen, HyunJi Nam, Suraj Nair, Chelsea Finn

Concretely, we propose an exploration technique, Batch Exploration with Examples (BEE), that explores relevant regions of the state-space, guided by a modest number of human provided images of important states.

Offline RL

Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift

no code implementations28 Sep 2020 Marvin Mengxin Zhang, Henrik Marklund, Nikita Dhawan, Abhishek Gupta, Sergey Levine, Chelsea Finn

A fundamental assumption of most machine learning algorithms is that the training and test data are drawn from the same underlying distribution.

Image Classification Meta-Learning

Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings

1 code implementation ICML 2020 Jesse Zhang, Brian Cheung, Chelsea Finn, Sergey Levine, Dinesh Jayaraman

Reinforcement learning (RL) in real-world safety-critical target settings like urban driving is hazardous, imperiling the RL agent, other agents, and the environment.

Offline Meta-Reinforcement Learning with Advantage Weighting

no code implementations13 Aug 2020 Eric Mitchell, Rafael Rafailov, Xue Bin Peng, Sergey Levine, Chelsea Finn

That is, in offline meta-RL, we meta-train on fixed, pre-collected data from several tasks in order to adapt to a new task with a very small amount (less than 5 trajectories) of data from the new task.

Machine Translation Meta-Learning +3

Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices

2 code implementations6 Aug 2020 Evan Zheran Liu, aditi raghunathan, Percy Liang, Chelsea Finn

Learning a new task often requires both exploring to gather task-relevant information and exploiting this information to solve the task.

Meta Reinforcement Learning Visual Navigation

Goal-Aware Prediction: Learning to Model What Matters

no code implementations ICML 2020 Suraj Nair, Silvio Savarese, Chelsea Finn

In this paper, we propose to direct prediction towards task relevant information, enabling the model to be aware of the current task and encouraging it to only model relevant quantities of the state space, resulting in a learning objective that more closely matches the downstream task.

Meta-Learning Symmetries by Reparameterization

2 code implementations ICLR 2021 Allan Zhou, Tom Knowles, Chelsea Finn

We present a method for learning and encoding equivariances into networks by learning corresponding parameter sharing patterns from data.

Meta-Learning

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.

Deep Reinforcement Learning amidst Lifelong Non-Stationarity

no code implementations ICML Workshop LifelongML 2020 Annie Xie, James Harrison, Chelsea Finn

As humans, our goals and our environment are persistently changing throughout our lifetime based on our experiences, actions, and internal and external drives.

Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabeling

no code implementations12 Jun 2020 Russell Mendonca, Xinyang Geng, Chelsea Finn, Sergey Levine

Our method is based on a simple insight: we recognize that dynamics models can be adapted efficiently and consistently with off-policy data, more easily than policies and value functions.

Meta Reinforcement Learning

Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning

no code implementations ICML Workshop LifelongML 2020 Evan Zheran Liu, aditi raghunathan, Percy Liang, Chelsea Finn

In principle, meta-reinforcement learning approaches can exploit this shared structure, but in practice, they fail to adapt to new environments when adaptation requires targeted exploration (e. g., exploring the cabinets to find ingredients in a new kitchen).

Meta Reinforcement Learning Visual Navigation

Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation

no code implementations ICML Workshop LifelongML 2020 Ryan Julian, Benjamin Swanson, Gaurav S. Sukhatme, Sergey Levine, Chelsea Finn, Karol Hausman

One of the great promises of robot learning systems is that they will be able to learn from their mistakes and continuously adapt to ever-changing environments, but most robot learning systems today are deployed as fixed policies which do not adapt after deployment.

Continual Learning Robotic Grasping

Model Based Reinforcement Learning for Atari

no code implementations ICLR 2020 Łukasz Kaiser, Mohammad Babaeizadeh, Piotr Miłos, Błażej Osiński, Roy H. Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski

We describe Simulated Policy Learning (SimPLe), a complete model-based deep RL algorithm based on video prediction models and present a comparison of several model architectures, including a novel architecture that yields the best results in our setting.

Atari Games Model-based Reinforcement Learning +1

Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning

no code implementations21 Apr 2020 Ryan Julian, Benjamin Swanson, Gaurav S. Sukhatme, Sergey Levine, Chelsea Finn, Karol Hausman

One of the great promises of robot learning systems is that they will be able to learn from their mistakes and continuously adapt to ever-changing environments.

Continual Learning Robotic Grasping

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.

Rapidly Adaptable Legged Robots via Evolutionary Meta-Learning

no code implementations2 Mar 2020 Xingyou Song, Yuxiang Yang, Krzysztof Choromanski, Ken Caluwaerts, Wenbo Gao, Chelsea Finn, Jie Tan

Learning adaptable policies is crucial for robots to operate autonomously in our complex and quickly changing world.

Legged Robots Meta-Learning

Scalable Multi-Task Imitation Learning with Autonomous Improvement

no code implementations25 Feb 2020 Avi Singh, Eric Jang, Alexander Irpan, Daniel Kappler, Murtaza Dalal, Sergey Levine, Mohi Khansari, Chelsea Finn

In this work, we target this challenge, aiming to build an imitation learning system that can continuously improve through autonomous data collection, while simultaneously avoiding the explicit use of reinforcement learning, to maintain the stability, simplicity, and scalability of supervised imitation.

Imitation Learning

Gradient Surgery for Multi-Task Learning

7 code implementations NeurIPS 2020 Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn

While deep learning and deep reinforcement learning (RL) systems have demonstrated impressive results in domains such as image classification, game playing, and robotic control, data efficiency remains a major challenge.

Image Classification Multi-Task Learning

Learning Predictive Models From Observation and Interaction

no code implementations ECCV 2020 Karl Schmeckpeper, Annie Xie, Oleh Rybkin, Stephen Tian, Kostas Daniilidis, Sergey Levine, Chelsea Finn

Learning predictive models from interaction with the world allows an agent, such as a robot, to learn about how the world works, and then use this learned model to plan coordinated sequences of actions to bring about desired outcomes.

Continuous Meta-Learning without Tasks

1 code implementation NeurIPS 2020 James Harrison, Apoorva Sharma, Chelsea Finn, Marco Pavone

In this work, we enable the application of generic meta-learning algorithms to settings where this task segmentation is unavailable, such as continual online learning with a time-varying task.

Image Classification Meta-Learning +1

Meta-Learning without Memorization

1 code implementation ICLR 2020 Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, Chelsea Finn

If this is not done, the meta-learner can ignore the task training data and learn a single model that performs all of the meta-training tasks zero-shot, but does not adapt effectively to new image classes.

Few-Shot Image Classification Meta-Learning

Unsupervised Curricula for Visual Meta-Reinforcement Learning

no code implementations NeurIPS 2019 Allan Jabri, Kyle Hsu, Ben Eysenbach, Abhishek Gupta, Sergey Levine, Chelsea Finn

In experiments on vision-based navigation and manipulation domains, we show that the algorithm allows for unsupervised meta-learning that transfers to downstream tasks specified by hand-crafted reward functions and serves as pre-training for more efficient supervised meta-learning of test task distributions.

Meta-Learning Meta Reinforcement Learning

Entity Abstraction in Visual Model-Based Reinforcement Learning

1 code implementation28 Oct 2019 Rishi Veerapaneni, John D. Co-Reyes, Michael Chang, Michael Janner, Chelsea Finn, Jiajun Wu, Joshua B. Tenenbaum, Sergey Levine

This paper tests the hypothesis that modeling a scene in terms of entities and their local interactions, as opposed to modeling the scene globally, provides a significant benefit in generalizing to physical tasks in a combinatorial space the learner has not encountered before.

Model-based Reinforcement Learning Object Discovery +2

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?

Video Prediction

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

5 code implementations24 Oct 2019 Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Avnish Narayan, Hayden Shively, Adithya Bellathur, Karol Hausman, Chelsea Finn, Sergey Levine

Therefore, if the aim of these methods is to enable faster acquisition of entirely new behaviors, we must evaluate them on task distributions that are sufficiently broad to enable generalization to new behaviors.

Meta-Learning Meta Reinforcement Learning +1

Mint: Matrix-Interleaving for Multi-Task Learning

no code implementations25 Sep 2019 Tianhe Yu, Saurabh Kumar, Eric Mitchell, Abhishek Gupta, Karol Hausman, Sergey Levine, Chelsea Finn

Deep learning enables training of large and flexible function approximators from scratch at the cost of large amounts of data.

Multi-Task Learning

Hope For The Best But Prepare For The Worst: Cautious Adaptation In RL Agents

no code implementations25 Sep 2019 Jesse Zhang, Brian Cheung, Chelsea Finn, Dinesh Jayaraman, Sergey Levine

We study the problem of safe adaptation: given a model trained on a variety of past experiences for some task, can this model learn to perform that task in a new situation while avoiding catastrophic failure?

Domain Adaptation Meta Reinforcement Learning

Consistent Meta-Reinforcement Learning via Model Identification and Experience Relabeling

no code implementations25 Sep 2019 Russell Mendonca, Xinyang Geng, Chelsea Finn, Sergey Levine

Reinforcement learning algorithms can acquire policies for complex tasks automatically, however the number of samples required to learn a diverse set of skills can be prohibitively large.

Meta Reinforcement Learning

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

Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation

1 code implementation ICLR 2020 Suraj Nair, Chelsea Finn

Video prediction models combined with planning algorithms have shown promise in enabling robots to learn to perform many vision-based tasks through only self-supervision, reaching novel goals in cluttered scenes with unseen objects.

Self-Supervised Learning Video Prediction

Meta-Learning with Implicit Gradients

4 code implementations NeurIPS 2019 Aravind Rajeswaran, Chelsea Finn, Sham Kakade, Sergey Levine

By drawing upon implicit differentiation, we develop the implicit MAML algorithm, which depends only on the solution to the inner level optimization and not the path taken by the inner loop optimizer.

Few-Shot Image Classification

Learning to Interactively Learn and Assist

no code implementations24 Jun 2019 Mark Woodward, Chelsea Finn, Karol Hausman

Most importantly, we find that our approach produces an agent that is capable of learning interactively from a human user, without a set of explicit demonstrations or a reward function, and achieving significantly better performance cooperatively with a human than a human performing the task alone.

Imitation Learning Question Answering

Training an Interactive Helper

no code implementations24 Jun 2019 Mark Woodward, Chelsea Finn, Karol Hausman

In this paper, we investigate if, and how, a "helper" agent can be trained to interactively adapt their behavior to maximize the reward of another agent, whom we call the "prime" agent, without observing their reward or receiving explicit demonstrations.

Meta-Learning

Language as an Abstraction for Hierarchical Deep Reinforcement Learning

2 code implementations NeurIPS 2019 Yiding Jiang, Shixiang Gu, Kevin Murphy, Chelsea Finn

We find that, using our approach, agents can learn to solve to diverse, temporally-extended tasks such as object sorting and multi-object rearrangement, including from raw pixel observations.

Few-Shot Intent Inference via Meta-Inverse Reinforcement Learning

no code implementations ICLR 2019 Kelvin Xu, Ellis Ratner, Anca Dragan, Sergey Levine, Chelsea Finn

A significant challenge for the practical application of reinforcement learning toreal world problems is the need to specify an oracle reward function that correctly defines a task.

End-to-End Robotic Reinforcement Learning without Reward Engineering

3 code implementations16 Apr 2019 Avi Singh, Larry Yang, Kristian Hartikainen, Chelsea Finn, Sergey Levine

In this paper, we propose an approach for removing the need for manual engineering of reward specifications by enabling a robot to learn from a modest number of examples of successful outcomes, followed by actively solicited queries, where the robot shows the user a state and asks for a label to determine whether that state represents successful completion of the task.

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

Guided Meta-Policy Search

no code implementations NeurIPS 2019 Russell Mendonca, Abhishek Gupta, Rosen Kralev, Pieter Abbeel, Sergey Levine, Chelsea Finn

Reinforcement learning (RL) algorithms have demonstrated promising results on complex tasks, yet often require impractical numbers of samples since they learn from scratch.

Continuous Control Imitation Learning +2

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.

NoRML: No-Reward Meta Learning

1 code implementation4 Mar 2019 Yuxiang Yang, Ken Caluwaerts, Atil Iscen, Jie Tan, Chelsea Finn

To this end, we introduce a method that allows for self-adaptation of learned policies: No-Reward Meta Learning (NoRML).

Meta-Learning

VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation

1 code implementation ICLR 2020 Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, Durk Kingma

Generative models that can model and predict sequences of future events can, in principle, learn to capture complex real-world phenomena, such as physical interactions.

Predict Future Video Frames Video Generation

Model-Based Reinforcement Learning for Atari

4 code implementations1 Mar 2019 Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H. Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski

We describe Simulated Policy Learning (SimPLe), a complete model-based deep RL algorithm based on video prediction models and present a comparison of several model architectures, including a novel architecture that yields the best results in our setting.

Atari Games Atari Games 100k +2

Online Meta-Learning

no code implementations ICLR Workshop LLD 2019 Chelsea Finn, Aravind Rajeswaran, Sham Kakade, Sergey Levine

Meta-learning views this problem as learning a prior over model parameters that is amenable for fast adaptation on a new task, but typically assumes the set of tasks are available together as a batch.

Meta-Learning

Unsupervised Visuomotor Control through Distributional Planning Networks

1 code implementation14 Feb 2019 Tianhe Yu, Gleb Shevchuk, Dorsa Sadigh, Chelsea Finn

While reinforcement learning (RL) has the potential to enable robots to autonomously acquire a wide range of skills, in practice, RL usually requires manual, per-task engineering of reward functions, especially in real world settings where aspects of the environment needed to compute progress are not directly accessible.

Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL

no code implementations ICLR 2019 Anusha Nagabandi, Chelsea Finn, Sergey Levine

The goal in this paper is to develop a method for continual online learning from an incoming stream of data, using deep neural network models.

Meta-Learning Model-based Reinforcement Learning

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.

One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks

no code implementations25 Oct 2018 Tianhe Yu, Pieter Abbeel, Sergey Levine, Chelsea Finn

We consider the problem of learning multi-stage vision-based tasks on a real robot from a single video of a human performing the task, while leveraging demonstration data of subtasks with other objects.

Imitation Learning

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

Unsupervised Learning via Meta-Learning

no code implementations ICLR 2019 Kyle Hsu, Sergey Levine, Chelsea Finn

A central goal of unsupervised learning is to acquire representations from unlabeled data or experience that can be used for more effective learning of downstream tasks from modest amounts of labeled data.

Meta-Learning

Time Reversal as Self-Supervision

no code implementations2 Oct 2018 Suraj Nair, Mohammad Babaeizadeh, Chelsea Finn, Sergey Levine, Vikash Kumar

We test our method on the domain of assembly, specifically the mating of tetris-style block pairs.

Few-Shot Goal Inference for Visuomotor Learning and Planning

no code implementations30 Sep 2018 Annie Xie, Avi Singh, Sergey Levine, Chelsea Finn

To that end, we formulate the few-shot objective learning problem, where the goal is to learn a task objective from only a few example images of successful end states for that task.

Visual Navigation

Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control

1 code implementation ICML 2018 Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, Chelsea Finn

A key challenge in complex visuomotor control is learning abstract representations that are effective for specifying goals, planning, and generalization.

Imitation Learning

Unsupervised Meta-Learning for Reinforcement Learning

no code implementations ICLR 2020 Abhishek Gupta, Benjamin Eysenbach, Chelsea Finn, Sergey Levine

In the context of reinforcement learning, meta-learning algorithms acquire reinforcement learning procedures to solve new problems more efficiently by utilizing experience from prior tasks.

Meta-Learning Meta Reinforcement Learning +1

Probabilistic Model-Agnostic Meta-Learning

1 code implementation NeurIPS 2018 Chelsea Finn, Kelvin Xu, Sergey Levine

However, a critical challenge in few-shot learning is task ambiguity: even when a powerful prior can be meta-learned from a large number of prior tasks, a small dataset for a new task can simply be too ambiguous to acquire a single model (e. g., a classifier) for that task that is accurate.

Active Learning Few-Shot Image Classification

Learning a Prior over Intent via Meta-Inverse Reinforcement Learning

no code implementations31 May 2018 Kelvin Xu, Ellis Ratner, Anca Dragan, Sergey Levine, Chelsea Finn

A significant challenge for the practical application of reinforcement learning in the real world is the need to specify an oracle reward function that correctly defines a task.

Stochastic Adversarial Video Prediction

3 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.

Representation Learning Video Generation +1

Universal Planning Networks

1 code implementation2 Apr 2018 Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, Chelsea Finn

We find that the representations learned are not only effective for goal-directed visual imitation via gradient-based trajectory optimization, but can also provide a metric for specifying goals using images.

Imitation Learning Representation Learning +1

Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning

2 code implementations ICLR 2019 Anusha Nagabandi, Ignasi Clavera, Simin Liu, Ronald S. Fearing, Pieter Abbeel, Sergey Levine, Chelsea Finn

Although reinforcement learning methods can achieve impressive results in simulation, the real world presents two major challenges: generating samples is exceedingly expensive, and unexpected perturbations or unseen situations cause proficient but specialized policies to fail at test time.

Continuous Control Meta-Learning +3

Recasting Gradient-Based Meta-Learning as Hierarchical Bayes

no code implementations ICLR 2018 Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, Thomas Griffiths

Meta-learning allows an intelligent agent to leverage prior learning episodes as a basis for quickly improving performance on a novel task.

Meta-Learning

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

Stochastic Variational Video Prediction

3 code implementations ICLR 2018 Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H. Campbell, Sergey Levine

We find that our proposed method produces substantially improved video predictions when compared to the same model without stochasticity, and to other stochastic video prediction methods.

Video Generation Video Prediction

Self-Supervised Visual Planning with Temporal Skip Connections

2 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

One-Shot Visual Imitation Learning via Meta-Learning

3 code implementations14 Sep 2017 Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, Sergey Levine

In this work, we present a meta-imitation learning method that enables a robot to learn how to learn more efficiently, allowing it to acquire new skills from just a single demonstration.

Imitation Learning Meta-Learning

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

64 code implementations ICML 2017 Chelsea Finn, Pieter Abbeel, Sergey Levine

We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning.

Few-Shot Image Classification General Classification +1

Active One-shot Learning

no code implementations21 Feb 2017 Mark Woodward, Chelsea Finn

Recent advances in one-shot learning have produced models that can learn from a handful of labeled examples, for passive classification and regression tasks.

General Classification One-Shot Learning

Generalizing Skills with Semi-Supervised Reinforcement Learning

no code implementations1 Dec 2016 Chelsea Finn, Tianhe Yu, Justin Fu, Pieter Abbeel, Sergey Levine

We evaluate our method on challenging tasks that require control directly from images, and show that our approach can improve the generalization of a learned deep neural network policy by using experience for which no reward function is available.

A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models

3 code implementations11 Nov 2016 Chelsea Finn, Paul Christiano, Pieter Abbeel, Sergey Levine

In particular, we demonstrate an equivalence between a sample-based algorithm for maximum entropy IRL and a GAN in which the generator's density can be evaluated and is provided as an additional input to the discriminator.

Imitation Learning

Reset-Free Guided Policy Search: Efficient Deep Reinforcement Learning with Stochastic Initial States

no code implementations4 Oct 2016 William Montgomery, Anurag Ajay, Chelsea Finn, Pieter Abbeel, Sergey Levine

Autonomous learning of robotic skills can allow general-purpose robots to learn wide behavioral repertoires without requiring extensive manual engineering.

Deep Visual Foresight for Planning Robot Motion

1 code implementation3 Oct 2016 Chelsea Finn, Sergey Levine

A key challenge in scaling up robot learning to many skills and environments is removing the need for human supervision, so that robots can collect their own data and improve their own performance without being limited by the cost of requesting human feedback.

Model-based Reinforcement Learning Video Prediction

Unsupervised Learning for Physical Interaction through Video Prediction

3 code implementations NeurIPS 2016 Chelsea Finn, Ian Goodfellow, Sergey Levine

A core challenge for an agent learning to interact with the world is to predict how its actions affect objects in its environment.

Video Prediction

Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization

3 code implementations1 Mar 2016 Chelsea Finn, Sergey Levine, Pieter Abbeel

We explore how inverse optimal control (IOC) can be used to learn behaviors from demonstrations, with applications to torque control of high-dimensional robotic systems.

Feature Engineering

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

Deep Spatial Autoencoders for Visuomotor Learning

1 code implementation21 Sep 2015 Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, Pieter Abbeel

Our method uses a deep spatial autoencoder to acquire a set of feature points that describe the environment for the current task, such as the positions of objects, and then learns a motion skill with these feature points using an efficient reinforcement learning method based on local linear models.

Learning Deep Neural Network Policies with Continuous Memory States

no code implementations5 Jul 2015 Marvin Zhang, Zoe McCarthy, Chelsea Finn, Sergey Levine, Pieter Abbeel

We evaluate our method on tasks involving continuous control in manipulation and navigation settings, and show that our method can learn complex policies that successfully complete a range of tasks that require memory.

Continuous Control

End-to-End Training of Deep Visuomotor Policies

no code implementations2 Apr 2015 Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel

Policy search methods can allow robots to learn control policies for a wide range of tasks, but practical applications of policy search often require hand-engineered components for perception, state estimation, and low-level control.

Learning Compact Convolutional Neural Networks with Nested Dropout

no code implementations22 Dec 2014 Chelsea Finn, Lisa Anne Hendricks, Trevor Darrell

Recently, nested dropout was proposed as a method for ordering representation units in autoencoders by their information content, without diminishing reconstruction cost.

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