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Found 6 papers, 2 papers with code
Hierarchical Decision Mamba
In this work, we introduce two novel methods, Decision Mamba (DM) and Hierarchical Decision Mamba (HDM), aimed at enhancing the performance of the Transformer models.
Music to Dance as Language Translation using Sequence Models
We train our method on AIST++ and PhantomDance data sets to teach a robotic arm to dance, but our method can be applied to a full humanoid robot.
DEFENDER: DTW-Based Episode Filtering Using Demonstrations for Enhancing RL Safety
We propose a task-agnostic method that leverages small sets of safe and unsafe demonstrations to improve the safety of RL agents during learning.
A Survey of Demonstration Learning
This paper provides a survey of demonstration learning, where we formally introduce the demonstration problem along with its main challenges and provide a comprehensive overview of the process of learning from demonstrations from the creation of the demonstration data set, to learning methods from demonstrations, and optimization by combining demonstration learning with different machine learning methods.
Hierarchical Decision Transformer
Our method outperforms the baselines in eight out of ten tasks of varied horizons and reward frequencies without prior task knowledge, showing the advantages of the hierarchical model approach for learning from demonstrations using a sequence model.
Contrastive Learning from Demonstrations
This paper presents a framework for learning visual representations from unlabeled video demonstrations captured from multiple viewpoints.
