Chelsea Finn

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Google ∙ berkeley college ∙ Stanford University

Research Scientist at Google Brain, Post-doctoral scholar at UC Berkeley

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Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings

Reinforcement learning (RL) in real-world safety-critical target setting...
08/15/2020 ∙ by Jesse Zhang, et al. ∙ 7 ∙ share

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Offline Meta-Reinforcement Learning with Advantage Weighting

Massive datasets have proven critical to successfully applying deep lear...
08/13/2020 ∙ by Eric Mitchell, et al. ∙ 31 ∙ share

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

We seek to efficiently learn by leveraging shared structure between diff...
08/06/2020 ∙ by Evan Zheran Liu, et al. ∙ 6 ∙ share

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Goal-Aware Prediction: Learning to Model What Matters

Learned dynamics models combined with both planning and policy learning ...
07/14/2020 ∙ by Suraj Nair, et al. ∙ 13 ∙ share

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Meta-Learning Symmetries by Reparameterization

Many successful deep learning architectures are equivariant to certain t...
07/06/2020 ∙ by Allan Zhou, et al. ∙ 5 ∙ share

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

A fundamental assumption of most machine learning algorithms is that the...
07/06/2020 ∙ by Marvin Zhang, et al. ∙ 5 ∙ share

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Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors

The ability to predict and plan into the future is fundamental for agent...
06/23/2020 ∙ by Karl Pertsch, et al. ∙ 8 ∙ share

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Deep Reinforcement Learning amidst Lifelong Non-Stationarity

As humans, our goals and our environment are persistently changing throu...
06/18/2020 ∙ by Annie Xie, et al. ∙ 0 ∙ share

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

Reinforcement learning algorithms can acquire policies for complex tasks...
06/12/2020 ∙ by Russell Mendonca, et al. ∙ 41 ∙ share

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MOPO: Model-based Offline Policy Optimization

Offline reinforcement learning (RL) refers to the problem of learning po...
05/27/2020 ∙ by Tianhe Yu, et al. ∙ 15 ∙ share

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

One of the great promises of robot learning systems is that they will be...
04/21/2020 ∙ by Ryan Julian, et al. ∙ 29 ∙ share

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Weakly-Supervised Reinforcement Learning for Controllable Behavior

Reinforcement learning (RL) is a powerful framework for learning to take...
04/06/2020 ∙ by Lisa Lee, et al. ∙ 11 ∙ share

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OmniTact: A Multi-Directional High Resolution Touch Sensor

Incorporating touch as a sensing modality for robots can enable finer an...
03/16/2020 ∙ by Akhil Padmanabha, et al. ∙ 2 ∙ share

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Rapidly Adaptable Legged Robots via Evolutionary Meta-Learning

Learning adaptable policies is crucial for robots to operate autonomousl...
03/02/2020 ∙ by Xingyou Song, et al. ∙ 6 ∙ share

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Scalable Multi-Task Imitation Learning with Autonomous Improvement

While robot learning has demonstrated promising results for enabling rob...
02/25/2020 ∙ by Avi Singh, et al. ∙ 0 ∙ share

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Gradient Surgery for Multi-Task Learning

While deep learning and deep reinforcement learning (RL) systems have de...
01/19/2020 ∙ by Tianhe Yu, et al. ∙ 19 ∙ share

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Learning Predictive Models From Observation and Interaction

Learning predictive models from interaction with the world allows an age...
12/30/2019 ∙ by Karl Schmeckpeper, et al. ∙ 12 ∙ share

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Continuous Meta-Learning without Tasks

Meta-learning is a promising strategy for learning to efficiently learn ...
12/18/2019 ∙ by James Harrison, et al. ∙ 15 ∙ share

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SMiRL: Surprise Minimizing RL in Dynamic Environments

All living organisms struggle against the forces of nature to carve out ...
12/11/2019 ∙ by Glen Berseth, et al. ∙ 8 ∙ share

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Unsupervised Curricula for Visual Meta-Reinforcement Learning

In principle, meta-reinforcement learning algorithms leverage experience...
12/09/2019 ∙ by Allan Jabri, et al. ∙ 7 ∙ share

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Meta-Learning without Memorization

The ability to learn new concepts with small amounts of data is a critic...
12/09/2019 ∙ by Mingzhang Yin, et al. ∙ 19 ∙ share

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Entity Abstraction in Visual Model-Based Reinforcement Learning

This paper tests the hypothesis that modeling a scene in terms of entiti...
10/28/2019 ∙ by Rishi Veerapaneni, et al. ∙ 33 ∙ share

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RoboNet: Large-Scale Multi-Robot Learning

Robot learning has emerged as a promising tool for taming the complexity...
10/24/2019 ∙ by Sudeep Dasari, et al. ∙ 33 ∙ share

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

Meta-reinforcement learning algorithms can enable robots to acquire new ...
10/24/2019 ∙ by Tianhe Yu, et al. ∙ 45 ∙ share

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Meta-Inverse Reinforcement Learning with Probabilistic Context Variables

Providing a suitable reward function to reinforcement learning can be di...
09/20/2019 ∙ by Lantao Yu, et al. ∙ 6 ∙ share

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

Video prediction models combined with planning algorithms have shown pro...
09/12/2019 ∙ by Suraj Nair, et al. ∙ 4 ∙ share

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Meta-Learning with Implicit Gradients

A core capability of intelligent systems is the ability to quickly learn...
09/10/2019 ∙ by Aravind Rajeswaran, et al. ∙ 2 ∙ share

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Learning to Interactively Learn and Assist

When deploying autonomous agents in the real world, we need to think abo...
06/24/2019 ∙ by Mark Woodward, et al. ∙ 0 ∙ share

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Training an Interactive Helper

Developing agents that can quickly adapt their behavior to new tasks rem...
06/24/2019 ∙ by Mark Woodward, et al. ∙ 1 ∙ share

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Language as an Abstraction for Hierarchical Deep Reinforcement Learning

Solving complex, temporally-extended tasks is a long-standing problem in...
06/18/2019 ∙ by Yiding Jiang, et al. ∙ 9 ∙ share

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Watch, Try, Learn: Meta-Learning from Demonstrations and Reward

Imitation learning allows agents to learn complex behaviors from demonst...
06/07/2019 ∙ by Allan Zhou, et al. ∙ 1 ∙ share

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End-to-End Robotic Reinforcement Learning without Reward Engineering

The combination of deep neural network models and reinforcement learning...
04/16/2019 ∙ by Avi Singh, et al. ∙ 8 ∙ share

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

Machine learning techniques have enabled robots to learn narrow, yet com...
04/11/2019 ∙ by Annie Xie, et al. ∙ 2 ∙ share

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Guided Meta-Policy Search

Reinforcement learning (RL) algorithms have demonstrated promising resul...
04/01/2019 ∙ by Russell Mendonca, et al. ∙ 8 ∙ share

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Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables

Deep reinforcement learning algorithms require large amounts of experien...
03/19/2019 ∙ by Kate Rakelly, et al. ∙ 32 ∙ share

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

Touch sensing is widely acknowledged to be important for dexterous robot...
03/11/2019 ∙ by Stephen Tian, et al. ∙ 2 ∙ share

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VideoFlow: A Flow-Based Generative Model for Video

Generative models that can model and predict sequences of future events ...
03/04/2019 ∙ by Manoj Kumar, et al. ∙ 6 ∙ share

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NoRML: No-Reward Meta Learning

Efficiently adapting to new environments and changes in dynamics is crit...
03/04/2019 ∙ by Yuxiang Yang, et al. ∙ 28 ∙ share

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Model-Based Reinforcement Learning for Atari

Model-free reinforcement learning (RL) can be used to learn effective po...
03/01/2019 ∙ by Łukasz Kaiser, et al. ∙ 20 ∙ share

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Online Meta-Learning

A central capability of intelligent systems is the ability to continuous...
02/22/2019 ∙ by Chelsea Finn, et al. ∙ 54 ∙ share

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Unsupervised Visuomotor Control through Distributional Planning Networks

While reinforcement learning (RL) has the potential to enable robots to ...
02/14/2019 ∙ by Tianhe Yu, et al. ∙ 6 ∙ share

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Reasoning About Physical Interactions with Object-Oriented Prediction and Planning

Object-based factorizations provide a useful level of abstraction for in...
12/28/2018 ∙ by Michael Janner, et al. ∙ 14 ∙ share

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

Humans and animals can learn complex predictive models that allow them t...
12/18/2018 ∙ by Anusha Nagabandi, et al. ∙ 4 ∙ share

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

Deep reinforcement learning (RL) algorithms can learn complex robotic sk...
12/03/2018 ∙ by Frederik Ebert, et al. ∙ 0 ∙ share

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One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks

We consider the problem of learning multi-stage vision-based tasks on a ...
10/25/2018 ∙ by Tianhe Yu, et al. ∙ 12 ∙ share

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

Prediction is an appealing objective for self-supervised learning of beh...
10/06/2018 ∙ by Frederik Ebert, et al. ∙ 2 ∙ share

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Unsupervised Learning via Meta-Learning

A central goal of unsupervised learning is to acquire representations fr...
10/04/2018 ∙ by Kyle Hsu, et al. ∙ 4 ∙ share

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Time Reversal as Self-Supervision

A longstanding challenge in robot learning for manipulation tasks has be...
10/02/2018 ∙ by Suraj Nair, et al. ∙ 4 ∙ share

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Few-Shot Goal Inference for Visuomotor Learning and Planning

Reinforcement learning and planning methods require an objective or rewa...
09/30/2018 ∙ by Annie Xie, et al. ∙ 0 ∙ share

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Unsupervised Meta-Learning for Reinforcement Learning

Meta-learning is a powerful tool that builds on multi-task learning to l...
06/12/2018 ∙ by Abhishek Gupta, et al. ∙ 0 ∙ share

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