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07/07/2023
Polybot: Training One Policy Across Robots While Embracing Variability
Reusing large datasets is crucial to scale vision-based robotic manipula...
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06/14/2023
Simple Embodied Language Learning as a Byproduct of Meta-Reinforcement Learning
Whereas machine learning models typically learn language by directly tra...
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01/26/2023
DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature
The fluency and factual knowledge of large language models (LLMs) height...
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01/19/2023
A Survey of Meta-Reinforcement Learning
While deep reinforcement learning (RL) has fueled multiple high-profile ...
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07/26/2022
Offline Reinforcement Learning at Multiple Frequencies
Leveraging many sources of offline robot data requires grappling with th...
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02/04/2022
BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning
In this paper, we study the problem of enabling a vision-based robotic m...
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10/26/2021
Meta-learning with an Adaptive Task Scheduler
To benefit the learning of a new task, meta-learning has been proposed t...
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09/21/2021
Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks
In this paper, we study the problem of learning a repertoire of low-leve...
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09/10/2021
Efficiently Identifying Task Groupings for Multi-Task Learning
Multi-task learning can leverage information learned by one task to bene...
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07/21/2021
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise
Annealed importance sampling (AIS) and related algorithms are highly eff...
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07/19/2021
Just Train Twice: Improving Group Robustness without Training Group Information
Standard training via empirical risk minimization (ERM) can produce mode...
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04/16/2021
MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale
General-purpose robotic systems must master a large repertoire of divers...
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12/14/2020
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Distribution shifts can cause significant degradation in a broad range o...
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05/27/2020
MOPO: Model-based Offline Policy Optimization
Offline reinforcement learning (RL) refers to the problem of learning po...
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12/11/2019
SMiRL: Surprise Minimizing RL in Dynamic Environments
All living organisms struggle against the forces of nature to carve out ...
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10/24/2019
RoboNet: Large-Scale Multi-Robot Learning
Robot learning has emerged as a promising tool for taming the complexity...
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03/04/2019
VideoFlow: A Flow-Based Generative Model for Video
Generative models that can model and predict sequences of future events ...
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03/30/2018
Learning to Adapt: Meta-Learning for Model-Based Control
Although reinforcement learning methods can achieve impressive results i...
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03/01/2016
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization
Reinforcement learning can acquire complex behaviors from high-level spe...
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04/02/2015
