8 papers
A Single Diffusion-Policy Controller for Multi-Task Block Pushing with Zero-Shot Sim-to-Real Transfer
Haitong Ma, Haldun Balim, Yang Hu +2
Diffusion policies have shown promising empirical performance in representing and learning complex maneuvers for robots using behavior cloning (BC). In this paper, we explore train…
GeMPO: Generalized Measure Matching for Online Diffusion Reinforcement Learning
Haitong Ma, Chenxiao Gao, Tianyi Chen +2
A commonly used family of RL algorithms for diffusion policies conducts softmax reweighting over samples from the behavior policy, which often induces an overgreedy policy and fail…
Spectral Ghost in Representation Learning: from Component Analysis to Self-Supervised Learning
Bo Dai, Na Li, Dale Schuurmans
Self-supervised learning (SSL) has improved empirical performance by unleashing the power of unlabeled data for practical applications. Specifically, SSL extracts the representatio…
Max-Entropy Reinforcement Learning with Flow Matching and A Case Study on LQR
Yuyang Zhang, Yang Hu, Bo Dai +1
Soft actor-critic (SAC) is a popular algorithm for max-entropy reinforcement learning. In practice, the energy-based policies in SAC are often approximated using simple policy clas…
One-Step Flow Policy Mirror Descent
Tianyi Chen, Haitong Ma, Na Li +2
Diffusion policies have achieved great success in online reinforcement learning (RL) due to their strong expressive capacity. However, the inference of diffusion policy models reli…
Stochastic Nonlinear Control via Finite-dimensional Spectral Dynamic Embedding
Zhaolin Ren, Tongzheng Ren, Haitong Ma +2
This paper proposes an approach, Spectral Dynamics Embedding Control (SDEC), to optimal control for nonlinear stochastic systems. This method reveals an infinite-dimensional featur…