Chelsea Finn
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Computer Science
Chelsea Finn's Degrees
- PhD Computer Science University of California, Berkeley
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(Suggest an Edit or Addition)Chelsea Finn's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks (2017) (7450)
- End-to-End Training of Deep Visuomotor Policies (2015) (2811)
- On the Opportunities and Risks of Foundation Models (2021) (938)
- Unsupervised Learning for Physical Interaction through Video Prediction (2016) (920)
- Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization (2016) (750)
- Deep visual foresight for planning robot motion (2016) (633)
- WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) (559)
- Model-Based Reinforcement Learning for Atari (2019) (545)
- Probabilistic Model-Agnostic Meta-Learning (2018) (519)
- Meta-Learning with Implicit Gradients (2019) (496)
- Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning (2019) (488)
- Deep spatial autoencoders for visuomotor learning (2015) (470)
- Stochastic Variational Video Prediction (2017) (437)
- Recasting Gradient-Based Meta-Learning as Hierarchical Bayes (2018) (419)
- One-Shot Visual Imitation Learning via Meta-Learning (2017) (416)
- MOPO: Model-based Offline Policy Optimization (2020) (410)
- Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables (2019) (407)
- Gradient Surgery for Multi-Task Learning (2020) (382)
- Stochastic Adversarial Video Prediction (2018) (368)
- Learning to Adapt in Dynamic, Real-World Environments through Meta-Reinforcement Learning (2018) (367)
- Online Meta-Learning (2019) (294)
- A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models (2016) (279)
- One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning (2018) (277)
- Self-Supervised Visual Planning with Temporal Skip Connections (2017) (259)
- Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control (2018) (250)
- Do As I Can, Not As I Say: Grounding Language in Robotic Affordances (2022) (231)
- Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm (2017) (198)
- End-to-End Robotic Reinforcement Learning without Reward Engineering (2019) (196)
- How to train your robot with deep reinforcement learning: lessons we have learned (2021) (192)
- Unsupervised Learning via Meta-Learning (2018) (186)
- Just Train Twice: Improving Group Robustness without Training Group Information (2021) (168)
- Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL (2018) (162)
- Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods (2018) (160)
- COMBO: Conservative Offline Model-Based Policy Optimization (2021) (159)
- RoboNet: Large-Scale Multi-Robot Learning (2019) (147)
- Entity Abstraction in Visual Model-Based Reinforcement Learning (2019) (142)
- Language as an Abstraction for Hierarchical Deep Reinforcement Learning (2019) (136)
- Universal Planning Networks (2018) (135)
- Adapting Deep Visuomotor Representations with Weak Pairwise Constraints (2015) (121)
- Meta-Learning without Memorization (2019) (120)
- MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale (2021) (120)
- Active One-shot Learning (2017) (115)
- VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation (2019) (108)
- VideoFlow: A Flow-Based Generative Model for Video (2019) (107)
- BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning (2022) (104)
- Reasoning About Physical Interactions with Object-Oriented Prediction and Planning (2018) (102)
- Recovery RL: Safe Reinforcement Learning With Learned Recovery Zones (2020) (96)
- Towards Adapting Deep Visuomotor Representations from Simulated to Real Environments (2015) (93)
- Manipulation by Feel: Touch-Based Control with Deep Predictive Models (2019) (92)
- Unsupervised Meta-Learning for Reinforcement Learning (2018) (88)
- Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control (2018) (83)
- R3M: A Universal Visual Representation for Robot Manipulation (2022) (83)
- Learning deep neural network policies with continuous memory states (2015) (79)
- Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation (2019) (79)
- Fast Model Editing at Scale (2021) (76)
- Learning to Adapt: Meta-Learning for Model-Based Control (2018) (76)
- Learning to Learn with Gradients (2018) (75)
- Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills (2021) (75)
- Learning Latent Representations to Influence Multi-Agent Interaction (2020) (72)
- Learning Visual Feature Spaces for Robotic Manipulation with Deep Spatial Autoencoders (2015) (68)
- Greedy Hierarchical Variational Autoencoders for Large-Scale Video Prediction (2021) (68)
- Offline Reinforcement Learning from Images with Latent Space Models (2020) (67)
- Efficiently Identifying Task Groupings for Multi-Task Learning (2021) (66)
- Learning to be Safe: Deep RL with a Safety Critic (2020) (65)
- Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresight (2019) (65)
- Few-Shot Goal Inference for Visuomotor Learning and Planning (2018) (62)
- Adaptive Risk Minimization: Learning to Adapt to Domain Shift (2020) (60)
- Rapidly Adaptable Legged Robots via Evolutionary Meta-Learning (2020) (58)
- Generalizing Skills with Semi-Supervised Reinforcement Learning (2016) (58)
- MEMO: Test Time Robustness via Adaptation and Augmentation (2021) (57)
- One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks (2018) (57)
- Continuous Meta-Learning without Tasks (2019) (56)
- Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning (2018) (55)
- Unsupervised Curricula for Visual Meta-Reinforcement Learning (2019) (54)
- Meta-Learning Symmetries by Reparameterization (2020) (53)
- OmniTact: A Multi-Directional High-Resolution Touch Sensor (2020) (53)
- Improving Out-of-Distribution Robustness via Selective Augmentation (2022) (50)
- One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL (2020) (49)
- Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation (2021) (49)
- Meta-Inverse Reinforcement Learning with Probabilistic Context Variables (2019) (48)
- Learning a Prior over Intent via Meta-Inverse Reinforcement Learning (2018) (47)
- Offline Meta-Reinforcement Learning with Advantage Weighting (2020) (45)
- Learning Generalizable Robotic Reward Functions from "In-The-Wild" Human Videos (2021) (44)
- Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift (2020) (44)
- Guided Meta-Policy Search (2019) (44)
- RT-1: Robotics Transformer for Real-World Control at Scale (2022) (43)
- Reinforcement Learning with Videos: Combining Offline Observations with Interaction (2020) (43)
- A Workflow for Offline Model-Free Robotic Reinforcement Learning (2021) (42)
- FitVid: Overfitting in Pixel-Level Video Prediction (2021) (42)
- Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets (2021) (41)
- Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning (2020) (40)
- Unsupervised Visuomotor Control through Distributional Planning Networks (2019) (39)
- Goal-Aware Prediction: Learning to Model What Matters (2020) (39)
- Correct-N-Contrast: A Contrastive Approach for Improving Robustness to Spurious Correlations (2022) (39)
- Extending the WILDS Benchmark for Unsupervised Adaptation (2021) (37)
- Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors (2020) (36)
- Deep Reinforcement Learning amidst Lifelong Non-Stationarity (2020) (35)
- Watch, Try, Learn: Meta-Learning from Demonstrations and Reward (2019) (35)
- Reset-free guided policy search: Efficient deep reinforcement learning with stochastic initial states (2016) (35)
- Model-Based Visual Planning with Self-Supervised Functional Distances (2020) (33)
- Learning Predictive Models From Observation and Interaction (2019) (33)
- Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices (2020) (32)
- Conservative Data Sharing for Multi-Task Offline Reinforcement Learning (2021) (30)
- Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabeling (2020) (30)
- Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings (2020) (30)
- MELD: Meta-Reinforcement Learning from Images via Latent State Models (2020) (29)
- NoRML: No-Reward Meta Learning (2019) (28)
- Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation (2020) (27)
- Continual Learning of Control Primitives: Skill Discovery via Reset-Games (2020) (26)
- Meta-Learning with Fewer Tasks through Task Interpolation (2021) (26)
- Reasoning About Physical Interactions with Object-Centric Models (2018) (24)
- DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature (2023) (23)
- Scalable Multi-Task Imitation Learning with Autonomous Improvement (2020) (23)
- Diversify and Disambiguate: Learning From Underspecified Data (2022) (21)
- Learning to Interactively Learn and Assist (2019) (21)
- Visual Adversarial Imitation Learning using Variational Models (2021) (20)
- Bridging text spotting and SLAM with junction features (2015) (20)
- Weakly-Supervised Reinforcement Learning for Controllable Behavior (2020) (19)
- How to Leverage Unlabeled Data in Offline Reinforcement Learning (2022) (18)
- Memory-Based Model Editing at Scale (2022) (18)
- Surgical Fine-Tuning Improves Adaptation to Distribution Shifts (2022) (18)
- SMiRL: Surprise Minimizing RL in Dynamic Environments (2019) (17)
- Policy Learning with Continuous Memory States for Partially Observed Robotic Control (2015) (17)
- SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments (2021) (17)
- Batch Exploration With Examples for Scalable Robotic Reinforcement Learning (2020) (16)
- Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks (2021) (15)
- Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms (2021) (15)
- Meta-learning with an Adaptive Task Scheduler (2021) (14)
- Time Reversal as Self-Supervision (2018) (13)
- Lifelong Robotic Reinforcement Learning by Retaining Experiences (2021) (13)
- Autonomous Reinforcement Learning via Subgoal Curricula (2021) (13)
- Deep Reinforcement Learning amidst Continual Structured Non-Stationarity (2021) (12)
- Vision-Based Manipulators Need to Also See from Their Hands (2022) (11)
- Noether Networks: Meta-Learning Useful Conserved Quantities (2021) (10)
- TRASS: Time Reversal as Self-Supervision (2020) (10)
- CoMPS: Continual Meta Policy Search (2021) (9)
- Policy Architectures for Compositional Generalization in Control (2022) (9)
- Measuring and Harnessing Transference in Multi-Task Learning (2020) (9)
- Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning (2020) (8)
- Multi-Task Reinforcement Learning without Interference (2019) (8)
- Catformer: Designing Stable Transformers via Sensitivity Analysis (2021) (8)
- Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time (2022) (8)
- Latent-Variable Advantage-Weighted Policy Optimization for Offline RL (2022) (8)
- Autonomous Reinforcement Learning: Formalism and Benchmarking (2021) (7)
- Do Deep Networks Transfer Invariances Across Classes? (2022) (7)
- A State-Distribution Matching Approach to Non-Episodic Reinforcement Learning (2022) (6)
- ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback (2021) (6)
- SMiRL: Surprise Minimizing Reinforcement Learning in Dynamic Environments (2019) (6)
- Play it by Ear: Learning Skills amidst Occlusion through Audio-Visual Imitation Learning (2022) (6)
- Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning (2020) (6)
- Beyond lowest-warping cost action selection in trajectory transfer (2015) (5)
- Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials (2022) (5)
- C-Mixup: Improving Generalization in Regression (2022) (5)
- Few-Shot Intent Inference via Meta-Inverse Reinforcement Learning (2018) (5)
- Exact (Then Approximate) Dynamic Programming for Deep Reinforcement Learning (2020) (4)
- Bayesian Embeddings for Few-Shot Open World Recognition (2021) (4)
- Persistent Reinforcement Learning via Subgoal Curricula (2021) (3)
- One-Shot Composition of Vision-Based Skills from Demonstration (2019) (3)
- Learning Compact Convolutional Neural Networks with Nested Dropout (2014) (3)
- Robust Policy Learning over Multiple Uncertainty Sets (2022) (3)
- Challenges of Acquiring Compositional Inductive Biases via Meta-Learning (2021) (3)
- Variable-Shot Adaptation for Online Meta-Learning (2020) (3)
- When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning (2022) (3)
- A Survey of Meta-Reinforcement Learning (2023) (3)
- SMiRL: Surprise Minimizing RL in Entropic Environments (2019) (2)
- Information is Power: Intrinsic Control via Information Capture (2021) (2)
- Self-Destructing Models: Increasing the Costs of Harmful Dual Uses in Foundation Models (2022) (2)
- NeRF in the Palm of Your Hand: Corrective Augmentation for Robotics via Novel-View Synthesis (2023) (2)
- Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning (2023) (2)
- Intrinsic Control of Variational Beliefs in Dynamic Partially-Observed Visual Environments (2021) (2)
- Knowledge-Driven New Drug Recommendation (2022) (1)
- Language-Driven Representation Learning for Robotics (2023) (1)
- Learning to Reinforcement Learn by Imitation (2018) (1)
- Offline Reinforcement Learning at Multiple Frequencies (2022) (1)
- Discriminator Augmented Model-Based Reinforcement Learning (2021) (1)
- Learning to Reason With Relational Abstractions (2022) (1)
- Learning Options via Compression (2022) (1)
- Goal-Conditioned Video Prediction (2019) (1)
- The Reflective Explorer: Online Meta-Exploration from Offline Data in Realistic Robotic Tasks (2021) (1)
- Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning (2023) (1)
- Train Offline, Test Online: A Real Robot Learning Benchmark (2022) (1)
- Training an Interactive Helper (2019) (1)
- Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference (2022) (1)
- Latent-Variable Advantage-Weighted Policy Optimization for Offline Reinforcement Learning (2022) (0)
- Leveraging Domain Relations for Domain Generalization (2023) (0)
- LAPO: Latent-Variable Advantage-Weighted Policy Optimization for Offline Reinforcement Learning (2022) (0)
- V ISION -B ASED M ANIPULATORS N EED TO A LSO S EE FROM T HEIR H ANDS (2022) (0)
- 2022 Source Labeled Unlabeled Belgium France Norway Validation China Target United States Canada UK Japan Mexico Extra (2022) (0)
- F AST M ODEL E DITING AT S CALE (2021) (0)
- Permutation Equivariant Neural Functionals (2023) (0)
- What Makes Representation Learning from Videos Hard for Control? (2022) (0)
- Consistent Meta-Reinforcement Learning via Model Identification and Experience Relabeling (2019) (0)
- Example-Based Offline Reinforcement Learning without Rewards (2021) (0)
- Mint: Matrix-Interleaving for Multi-Task Learning (2019) (0)
- Decoupled Meta-Learning with Structured Latents (2019) (0)
- Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware (2023) (0)
- Hope For The Best But Prepare For The Worst: Cautious Adaptation In RL Agents (2019) (0)
- Predict futures using video prediction model Training Time Test Time Collect Kinesthetic Demonstrations Autonomous Random Data (0)
- Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models (2022) (0)
- Extended Abstract: Concept Acquisition Through Meta-Learning (2017) (0)
- A Control-Centric Benchmark for Video Prediction (2023) (0)
- Self-Supervised Learning of Object Motion Through Adversarial Video Prediction (2018) (0)
- A UTONOMOUS R EINFORCEMENT L EARNING : F ORMALISM AND B ENCHMARKING (2022) (0)
- Project and Probe: Sample-Efficient Domain Adaptation by Interpolating Orthogonal Features (2023) (0)
- Structure & Priors in Reinforcement Learning (SPiRL) (2019) (0)
- You Only Live Once: Single-Life Reinforcement Learning (2022) (0)
- Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts (2023) (0)
- VARIATIONAL MODEL-BASED IMITATION LEARNING IN HIGH-DIMENSIONAL OBSERVATION SPACES (2021) (0)
- Universal Planning Networks-Long Version + Supplementary (2018) (0)
- Multi-Domain Long-Tailed Learning by Augmenting Disentangled Representations (2022) (0)
- MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance (2021) (0)
- Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets (2023) (0)
- S Tochastic V Ariational V Ideo P Rediction (2018) (0)
- Multi-Task Offline Reinforcement Learning with Conservative Data Sharing (2021) (0)
- Open-World Object Manipulation using Pre-trained Vision-Language Models (2023) (0)
- Learned Tactile Dynamics Candidate actions Sequence Prediction Compute Costs Choose Best Action Model Predictive Control (2019) (0)
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