4 papers
A Multimodal Label Forecasting Method for Aperiodic Visuo-Motor Time Series
Borui He, Garrett E Katz
Deep learning models have been increasingly applied to Time Series Forecasting (TSF) in recent years. Transformer-based and MLP-based models have both been used effectively on many…
Closing the Loop on the Poppy Humanoid: Bipedal Locomotion with Linear-Quadratic Control and Learned Cost Functions
Xulin Chen, Borui He, Ruipeng Liu +3
The Poppy Humanoid is an open-source, low-cost robot suitable for research and education in artificial intelligence. However, we are unaware of any published methodology that achie…
Beyond Coefficients: Forecast-Necessity Testing for Interpretable Causal Discovery in Nonlinear Time-Series Models
Valentina Kuskova, Dmitry Zaytsev, Michael Coppedge
Nonlinear machine-learning models are increasingly used to discover causal relationships in time-series data, yet the interpretation of their outputs remains poorly understood. In…
Lipschitz-Regularized Critics Lead to Policy Robustness Against Transition Dynamics Uncertainty
Xulin Chen, Ruipeng Liu, Zhenyu Gan +1
Uncertainties in transition dynamics pose a critical challenge in reinforcement learning (RL), often resulting in performance degradation of trained policies when deployed on hardw…