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Found 5 papers, 2 papers with code
BridgeData V2: A Dataset for Robot Learning at Scale
By publicly sharing BridgeData V2 and our pre-trained models, we aim to accelerate research in scalable robot learning methods.
Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control
Our method achieves robust performance in the real world by learning an embedding from the labeled data that aligns language not to the goal image, but rather to the desired change between the start and goal images that the instruction corresponds to.
IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies
In this paper, we reinterpret IQL as an actor-critic method by generalizing the critic objective and connecting it to a behavior-regularized implicit actor.
Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning
We learn this representation using a metric form of this abstraction, and show its ability to generalize to new goals in simulation manipulation tasks.
GEM: Group Enhanced Model for Learning Dynamical Control Systems
In this work, we take advantage of these structures to build effective dynamical models that are amenable to sample-based learning.
