Diffusion models have demonstrated highly-expressive generative capabilities in vision and
NLP. Recent studies in reinforcement learning (RL) have shown that diffusion models are …

Nowadays, open-source large language models like LLaMA have emerged. Recent
developments have incorporated supervised fine-tuning (SFT) and reinforcement learning …

This paper provides a comprehensive review of the current status, advancements, and future
prospects of humanoid robots, highlighting their significance in driving the evolution of next …

Offline reinforcement learning (RL) methods can generally be categorized into two types: RL-
based and Imitation-based. RL-based methods could in principle enjoy out-of-distribution …

In this paper, we present ContExtual Imitation Learning (CEIL), a general and broadly
applicable algorithm for imitation learning (IL). Inspired by the formulation of hindsight …

Imitation learning from a large set of human demonstrations has proved to be an effective
paradigm for building capable robot agents. However, the demonstrations can be extremely …

Offline reinforcement learning (RL) enables learning a decision-making policy without
interaction with the environment. This makes it particularly beneficial in situations where …

With the rapid growth of computing powers and recent advances in deep learning, we have
witnessed impressive demonstrations of novel robot capabilities in research settings …

Imitation learning (IL) algorithms excel in acquiring high-quality policies from expert data for
sequential decision-making tasks. But, their effectiveness is hampered when faced with …

Pre-training with offline data and online fine-tuning using reinforcement learning is a
promising strategy for learning control policies by leveraging the best of both worlds in terms …