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Found 11 papers, 4 papers with code
Video as the New Language for Real-World Decision Making
Moreover, we demonstrate how, like language models, video generation can serve as planners, agents, compute engines, and environment simulators through techniques such as in-context learning, planning and reinforcement learning.
Genie: Generative Interactive Environments
We introduce Genie, the first generative interactive environment trained in an unsupervised manner from unlabelled Internet videos.
A Generalist Dynamics Model for Control
We investigate the use of transformer sequence models as dynamics models (TDMs) for control.
Accelerating exploration and representation learning with offline pre-training
In this work, we follow the hypothesis that exploration and representation learning can be improved by separately learning two different models from a single offline dataset.
A Generalist Agent
Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs.
Ranked #1 on
Skill Generalization
on RGB-Stacking
Imitation by Predicting Observations
Imitation learning enables agents to reuse and adapt the hard-won expertise of others, offering a solution to several key challenges in learning behavior.
Evaluating task-agnostic exploration for fixed-batch learning of arbitrary future tasks
Deep reinforcement learning has been shown to solve challenging tasks where large amounts of training experience is available, usually obtained online while learning the task.
Ctrl-Z: Recovering from Instability in Reinforcement Learning
When learning behavior, training data is often generated by the learner itself; this can result in unstable training dynamics, and this problem has particularly important applications in safety-sensitive real-world control tasks such as robotics.
Sim-to-Real Transfer of Robot Learning with Variable Length Inputs
Current end-to-end deep Reinforcement Learning (RL) approaches require jointly learning perception, decision-making and low-level control from very sparse reward signals and high-dimensional inputs, with little capability of incorporating prior knowledge.
One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay
Recently, model-free reinforcement learning algorithms have been shown to solve challenging problems by learning from extensive interaction with the environment.
Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments
This is significant because a robot interpreting a natural-language navigation instruction on the basis of what it sees is carrying out a vision and language process that is similar to Visual Question Answering.
Ranked #10 on
Visual Navigation
on R2R
