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

We are motivated by the goal of generalist robots that can complete a wi...
03/31/2021 ∙ by Annie S. Chen, et al. ∙ 0 ∙ share

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Discriminator Augmented Model-Based Reinforcement Learning

By planning through a learned dynamics model, model-based reinforcement ...
03/24/2021 ∙ by Behzad Haghgoo, et al. ∙ 9 ∙ share

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Greedy Hierarchical Variational Autoencoders for Large-Scale Video Prediction

A video prediction model that generalizes to diverse scenes would enable...
03/06/2021 ∙ by Bohan Wu, et al. ∙ 11 ∙ share

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

Reinforcement learning methods can achieve significant performance but r...
03/05/2021 ∙ by Ali Ghadirzadeh, et al. ∙ 13 ∙ share

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COMBO: Conservative Offline Model-Based Policy Optimization

Model-based algorithms, which learn a dynamics model from logged experie...
02/16/2021 ∙ by Tianhe Yu, et al. ∙ 0 ∙ share

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

Deep reinforcement learning (RL) has emerged as a promising approach for...
02/04/2021 ∙ by Julian Ibarz, et al. ∙ 48 ∙ share

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Model-Based Visual Planning with Self-Supervised Functional Distances

A generalist robot must be able to complete a variety of tasks in its en...
12/30/2020 ∙ by Stephen Tian, et al. ∙ 13 ∙ share

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Offline Reinforcement Learning from Images with Latent Space Models

Offline reinforcement learning (RL) refers to the problem of learning po...
12/21/2020 ∙ by Rafael Rafailov, et al. ∙ 10 ∙ share

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Variable-Shot Adaptation for Online Meta-Learning

Few-shot meta-learning methods consider the problem of learning new task...
12/14/2020 ∙ by Tianhe Yu, et al. ∙ 5 ∙ share

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WILDS: A Benchmark of in-the-Wild Distribution Shifts

Distribution shifts can cause significant degradation in a broad range o...
12/14/2020 ∙ by Pang Wei Koh, et al. ∙ 11 ∙ share

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

Model-based reinforcement learning (MBRL) methods have shown strong samp...
12/08/2020 ∙ by Mohammad Babaeizadeh, et al. ∙ 7 ∙ share

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Learning Latent Representations to Influence Multi-Agent Interaction

Seamlessly interacting with humans or robots is hard because these agent...
11/12/2020 ∙ by Annie Xie, et al. ∙ 3 ∙ share

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Reinforcement Learning with Videos: Combining Offline Observations with Interaction

Reinforcement learning is a powerful framework for robots to acquire ski...
11/12/2020 ∙ by Karl Schmeckpeper, et al. ∙ 15 ∙ share

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

Reinforcement learning has the potential to automate the acquisition of ...
11/10/2020 ∙ by Kelvin Xu, et al. ∙ 0 ∙ share

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Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones

Safety remains a central obstacle preventing widespread use of RL in the...
10/29/2020 ∙ by Brijen Thananjeyan, et al. ∙ 15 ∙ share

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Measuring and Harnessing Transference in Multi-Task Learning

Multi-task learning can leverage information learned by one task to bene...
10/29/2020 ∙ by Christopher Fifty, et al. ∙ 6 ∙ share

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Learning to be Safe: Deep RL with a Safety Critic

Safety is an essential component for deploying reinforcement learning (R...
10/27/2020 ∙ by Krishnan Srinivasan, et al. ∙ 3 ∙ share

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

While reinforcement learning algorithms can learn effective policies for...
10/27/2020 ∙ by Saurabh Kumar, et al. ∙ 4 ∙ share

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

Meta-reinforcement learning algorithms can enable autonomous agents, suc...
10/26/2020 ∙ by Tony Z. Zhao, et al. ∙ 12 ∙ share

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Batch Exploration with Examples for Scalable Robotic Reinforcement Learning

Learning from diverse offline datasets is a promising path towards learn...
10/22/2020 ∙ by Annie S. Chen, et al. ∙ 6 ∙ share

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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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