17 citations · 90 across the 70 of their papers we have counts for
5 papers · 1 filter
ODEWorld: A Continuous Predictive Architecture via Physical-Time Flow
Dongxiu Liu, Haoyi Niu, Peng Cheng +5
In the physical world we inhabit, space and time are fundamentally continuous. However, existing machine learning paradigms for world modeling are largely confined to discrete-time…
Embedding Hybrid Systems into Continuous Latent Vector Fields
Sangli Teng, Hang Liu, Koushil Sreenath
This work proves that an -dimensional hybrid system can be embedded into an -dimensional Euclidean space equipped with a continuous vector field on its embedded image wheneve…
Robust Adversarial Policy Optimization Under Dynamics Uncertainty
Mintae Kim, Koushil Sreenath
Reinforcement learning (RL) policies often fail under dynamics that differ from training, a gap not fully addressed by domain randomization or existing adversarial RL methods. Dist…
WOMBET: World Model-Based Experience Transfer for Robust and Sample-efficient Reinforcement Learning
Mintae Kim, Koushil Sreenath
Reinforcement learning (RL) in robotics is often limited by the cost and risk of data collection, motivating experience transfer from a source task to a target task. Offline-to-onl…
CHyLL: Learning Continuous Neural Representations of Hybrid Systems
Sangli Teng, Hang Liu, Jingyu Song +1
Learning the flows of hybrid systems that have both continuous and discrete time dynamics is challenging. The existing method learns the dynamics in each discrete mode, which suffe…