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 …

Probabilistic dynamics model ensemble is widely used in existing model-based
reinforcement learning methods as it outperforms a single dynamics model in both …

Learning from observation (LfO) aims to imitate experts by learning from state-only
demonstrations without requiring action labels. Existing adversarial imitation learning …

This survey presents an overview of methods for learning from video (LfV) in the context of
reinforcement learning (RL) and robotics. We focus on methods capable of scaling to large …

Humans often acquire new skills through observation and imitation. For robotic agents,
learning from the plethora of unlabeled video demonstration data available on the Internet …

Deep reinforcement learning has shown promising application effects in many fields.
However, issues such as low sample efficiency and weak knowledge transfer and …

We present a framework for learning useful subgoals that support efficient long-term
planning to achieve novel goals. At the core of our framework is a collection of rational …

The in-context learning ability of Transformer models has brought new possibilities to visual
navigation. In this paper, we focus on the video navigation setting, where an in-context …

Model predictive control (MPC) is a popular approach for trajectory optimization in practical
robotics applications. MPC policies can optimize trajectory parameters under kinodynamic …