7 papers
AnyPos: Automated Task-Agnostic Actions for Bimanual Manipulation
Hengkai Tan, Yao Feng, Xinyi Mao +5
Learning generalizable manipulation policies hinges on data, yet robot manipulation data is scarce and often entangled with specific embodiments, making both cross-task and cross-p…
Task Aware Dreamer for Task Generalization in Reinforcement Learning
Chengyang Ying, Xinning Zhou, Zhongkai Hao +4
A long-standing goal of reinforcement learning is to acquire agents that can learn on training tasks and generalize well on unseen tasks that may share a similar dynamic but with d…
Accelerating PDE-Constrained Optimization by the Derivative of Neural Operators
Ze Cheng, Zhuoyu Li, Xiaoqiang Wang +4
PDE-Constrained Optimization (PDECO) problems can be accelerated significantly by employing gradient-based methods with surrogate models like neural operators compared to tradition…
Exploratory Diffusion Model for Unsupervised Reinforcement Learning
Chengyang Ying, Huayu Chen, Xinning Zhou +3
Unsupervised reinforcement learning (URL) aims to pre-train agents by exploring diverse states or skills in reward-free environments, facilitating efficient adaptation to downstrea…
Your Diffusion Model is Secretly a Certifiably Robust Classifier
Huanran Chen, Yinpeng Dong, Shitong Shao +4
Generative learning, recognized for its effective modeling of data distributions, offers inherent advantages in handling out-of-distribution instances, especially for enhancing rob…
Improved Operator Learning by Orthogonal Attention
Zipeng Xiao, Zhongkai Hao, Bokai Lin +2
Neural operators, as an efficient surrogate model for learning the solutions of PDEs, have received extensive attention in the field of scientific machine learning. Among them, att…