
-
Model-Based Visual Planning with Self-Supervised Functional Distances
A generalist robot must be able to complete a variety of tasks in its en...
read it
-
Offline Reinforcement Learning from Images with Latent Space Models
Offline reinforcement learning (RL) refers to the problem of learning po...
read it
-
Variable-Shot Adaptation for Online Meta-Learning
Few-shot meta-learning methods consider the problem of learning new task...
read it
-
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Distribution shifts can cause significant degradation in a broad range o...
read it
-
Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) methods have shown strong samp...
read it
-
Learning Latent Representations to Influence Multi-Agent Interaction
Seamlessly interacting with humans or robots is hard because these agent...
read it
-
Reinforcement Learning with Videos: Combining Offline Observations with Interaction
Reinforcement learning is a powerful framework for robots to acquire ski...
read it
-
Continual Learning of Control Primitives: Skill Discovery via Reset-Games
Reinforcement learning has the potential to automate the acquisition of ...
read it
-
Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones
Safety remains a central obstacle preventing widespread use of RL in the...
read it
-
Measuring and Harnessing Transference in Multi-Task Learning
Multi-task learning can leverage information learned by one task to bene...
read it
-
Learning to be Safe: Deep RL with a Safety Critic
Safety is an essential component for deploying reinforcement learning (R...
read it
-
One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL
While reinforcement learning algorithms can learn effective policies for...
read it
-
MELD: Meta-Reinforcement Learning from Images via Latent State Models
Meta-reinforcement learning algorithms can enable autonomous agents, suc...
read it
-
Batch Exploration with Examples for Scalable Robotic Reinforcement Learning
Learning from diverse offline datasets is a promising path towards learn...
read it
-
Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings
Reinforcement learning (RL) in real-world safety-critical target setting...
read it
-
Offline Meta-Reinforcement Learning with Advantage Weighting
Massive datasets have proven critical to successfully applying deep lear...
read it
-
Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning
We seek to efficiently learn by leveraging shared structure between diff...
read it
-
Goal-Aware Prediction: Learning to Model What Matters
Learned dynamics models combined with both planning and policy learning ...
read it
-
Meta-Learning Symmetries by Reparameterization
Many successful deep learning architectures are equivariant to certain t...
read it
-
Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift
A fundamental assumption of most machine learning algorithms is that the...
read it
-
Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors
The ability to predict and plan into the future is fundamental for agent...
read it
-
Deep Reinforcement Learning amidst Lifelong Non-Stationarity
As humans, our goals and our environment are persistently changing throu...
read it
-
Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabeling
Reinforcement learning algorithms can acquire policies for complex tasks...
read it
-
MOPO: Model-based Offline Policy Optimization
Offline reinforcement learning (RL) refers to the problem of learning po...
read it
-
Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation
One of the great promises of robot learning systems is that they will be...
read it
-
Weakly-Supervised Reinforcement Learning for Controllable Behavior
Reinforcement learning (RL) is a powerful framework for learning to take...
read it
-
OmniTact: A Multi-Directional High Resolution Touch Sensor
Incorporating touch as a sensing modality for robots can enable finer an...
read it
-
Rapidly Adaptable Legged Robots via Evolutionary Meta-Learning
Learning adaptable policies is crucial for robots to operate autonomousl...
read it
-
Scalable Multi-Task Imitation Learning with Autonomous Improvement
While robot learning has demonstrated promising results for enabling rob...
read it
-
Gradient Surgery for Multi-Task Learning
While deep learning and deep reinforcement learning (RL) systems have de...
read it
-
Learning Predictive Models From Observation and Interaction
Learning predictive models from interaction with the world allows an age...
read it
-
Continuous Meta-Learning without Tasks
Meta-learning is a promising strategy for learning to efficiently learn ...
read it
-
SMiRL: Surprise Minimizing RL in Dynamic Environments
All living organisms struggle against the forces of nature to carve out ...
read it
-
Unsupervised Curricula for Visual Meta-Reinforcement Learning
In principle, meta-reinforcement learning algorithms leverage experience...
read it
-
Meta-Learning without Memorization
The ability to learn new concepts with small amounts of data is a critic...
read it
-
Entity Abstraction in Visual Model-Based Reinforcement Learning
This paper tests the hypothesis that modeling a scene in terms of entiti...
read it
-
RoboNet: Large-Scale Multi-Robot Learning
Robot learning has emerged as a promising tool for taming the complexity...
read it
-
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
Meta-reinforcement learning algorithms can enable robots to acquire new ...
read it
-
Meta-Inverse Reinforcement Learning with Probabilistic Context Variables
Providing a suitable reward function to reinforcement learning can be di...
read it
-
Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation
Video prediction models combined with planning algorithms have shown pro...
read it
-
Meta-Learning with Implicit Gradients
A core capability of intelligent systems is the ability to quickly learn...
read it
-
Learning to Interactively Learn and Assist
When deploying autonomous agents in the real world, we need to think abo...
read it
-
Training an Interactive Helper
Developing agents that can quickly adapt their behavior to new tasks rem...
read it
-
Language as an Abstraction for Hierarchical Deep Reinforcement Learning
Solving complex, temporally-extended tasks is a long-standing problem in...
read it
-
Watch, Try, Learn: Meta-Learning from Demonstrations and Reward
Imitation learning allows agents to learn complex behaviors from demonst...
read it
-
End-to-End Robotic Reinforcement Learning without Reward Engineering
The combination of deep neural network models and reinforcement learning...
read it
-
Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresight
Machine learning techniques have enabled robots to learn narrow, yet com...
read it
-
Guided Meta-Policy Search
Reinforcement learning (RL) algorithms have demonstrated promising resul...
read it
-
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
Deep reinforcement learning algorithms require large amounts of experien...
read it
-
Manipulation by Feel: Touch-Based Control with Deep Predictive Models
Touch sensing is widely acknowledged to be important for dexterous robot...
read it