15 papers
Round-Trip Consistency: Bidirectional Diffusion Models Can Predict Their Own Rollout Errors
Alexander Scheinker
Autoregressive models accumulate error over long rollouts, yet at deployment there is no ground truth to measure it against. We train a single conditional latent diffusion model th…
Bidirectional Autoregressive Latent Diffusion for Forward and Inverse Magnetohydrodynamics
Alexander Scheinker
This work presents a new bidirectional autoregressive latent diffusion approach for predicting the evolution of multiple fields (mass density, pressure, velocity, and magnetic fiel…
Mahalanobis-Guided Latent OOD Detection for Hybrid ES-DRL Control in Time-Varying Systems
Shaifalee Saxena, Alexander Scheinker
In this paper, we study Mahalanobis-guided latent out-of-distribution (OOD) detection for test-time RL controller switching in nonlinear time-varying systems. RL controllers can qu…
Deep Reinforcement Learning for Robotic Manipulation under Distribution Shift with Bounded Extremum Seeking
Shaifalee Saxena, Rafael Fierro, Alexander Scheinker
Reinforcement learning has shown strong performance in robotic manipulation, but learned policies often degrade in performance when test conditions differ from the training distrib…
Nested Extremum Seeking Converges to Stackelberg Equilibrium
Brad Ratto, Alan Williams, Miroslav KrstiÄ +2
The nested Extremum Seeking (nES) algorithm is a model-free optimization method that has been shown to converge to a neighborhood of a Nash equilibrium. In this work, we demonstrat…
Improved Robustness of Deep Reinforcement Learning for Control of Time-Varying Systems by Bounded Extremum Seeking
Shaifalee Saxena, Alan Williams, Rafael Fierro +1
In this paper, we study the use of robust model independent bounded extremum seeking (ES) feedback control to improve the robustness of deep reinforcement learning (DRL) controller…