Abstract Many problems in Reinforcement Learning (RL) seek an optimal policy with large
discrete multidimensional yet unordered action spaces; these include problems in …

This paper provides a comprehensive introduction to Reinforcement Learning (RL),
summarizes recent developments that showed remarkable success, and discusses their …

In the context of interaction with unmodelled systems, it becomes imperative for a robot
controller to possess the capability to dynamically adjust its actions in real-time, enhancing …

Although reinforcement learning has found widespread use in dense reward settings,
training autonomous agents with sparse rewards remains challenging. To address this …

Deep reinforcement learning (DRL) has been successfully used to solve various robotic
manipulation tasks. However, most of the existing works do not address the issue of control …

Embedding an optimization process has been explored for imposing efficient and flexible
policy structures. Existing work often build upon nonlinear optimization with explicitly …

Diffusion-based visuomotor policies excel at learning complex robotic tasks by effectively
combining visual data with high-dimensional, multi-modal action distributions. However …

We develop two consensus-based learning algorithms for multi-robot systems applied on
complex tasks involving collision constraints and force interactions, such as the cooperative …

We introduce Riemannian Flow Matching Policies (RFMP), a novel model for learning and
synthesizing robot visuomotor policies. RFMP leverages the efficient training and inference …

Though robot learning is often formulated in terms of discrete-time Markov decision
processes (MDPs), physical robots require near-continuous multiscale feedback control …