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Found 7 papers, 3 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.
Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data
Robotic systems that rely primarily on self-supervised learning have the potential to decrease the amount of human annotation and engineering effort required to learn control strategies.
Generalization with Lossy Affordances: Leveraging Broad Offline Data for Learning Visuomotor Tasks
The utilization of broad datasets has proven to be crucial for generalization for a wide range of fields.
Don't Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning
Reinforcement learning (RL) algorithms hold the promise of enabling autonomous skill acquisition for robotic systems.
Learning Finite Linear Temporal Logic Specifications with a Specialized Neural Operator
Finite linear temporal logic ($\mathsf{LTL}_f$) is a powerful formal representation for modeling temporal sequences.
PLAD: Learning to Infer Shape Programs with Pseudo-Labels and Approximate Distributions
Related to these approaches, we introduce a novel self-training variant unique to program inference, where program pseudo-labels are paired with their executed output shapes, avoiding label mismatch at the cost of an approximate shape distribution.
