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Found 4 papers, 1 papers with code
RiEMann: Near Real-Time SE(3)-Equivariant Robot Manipulation without Point Cloud Segmentation
RiEMann learns a manipulation task from scratch with 5 to 10 demonstrations, generalizes to unseen SE(3) transformations and instances of target objects, resists visual interference of distracting objects, and follows the near real-time pose change of the target object.
Transfering Hierarchical Structure with Dual Meta Imitation Learning
Hierarchical Imitation Learning (HIL) is an effective way for robots to learn sub-skills from long-horizon unsegmented demonstrations.
Transferring Hierarchical Structure with Dual Meta Imitation Learning
Hierarchical Imitation learning (HIL) is an effective way for robots to learn sub-skills from long-horizon unsegmented demonstrations.
CRIL: Continual Robot Imitation Learning via Generative and Prediction Model
Imitation learning (IL) algorithms have shown promising results for robots to learn skills from expert demonstrations.
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