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Found 5 papers, 3 papers with code
From LLMs to Actions: Latent Codes as Bridges in Hierarchical Robot Control
This enables LLMs to flexibly communicate goals in the task plan without being entirely constrained by language limitations.
Interactive Task Planning with Language Models
An interactive robot framework accomplishes long-horizon task planning and can easily generalize to new goals or distinct tasks, even during execution.
Masked Trajectory Models for Prediction, Representation, and Control
We introduce Masked Trajectory Models (MTM) as a generic abstraction for sequential decision making.
RoboPianist: Dexterous Piano Playing with Deep Reinforcement Learning
Replicating human-like dexterity in robot hands represents one of the largest open problems in robotics.
DayDreamer: World Models for Physical Robot Learning
Learning a world model to predict the outcomes of potential actions enables planning in imagination, reducing the amount of trial and error needed in the real environment.
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