3 papers
cs.LG2025
Dense Dynamics-Aware Reward Synthesis: Integrating Prior Experience with Demonstrations
Cevahir Koprulu, Po-han Li, Tianyu Qiu +5
Many continuous control problems can be formulated as sparse-reward reinforcement learning (RL) tasks. In principle, online RL methods can automatically explore the state space to…
cs.RO2024
Learning to Walk from Three Minutes of Real-World Data with Semi-structured Dynamics Models
Jacob Levy, Tyler Westenbroek, David Fridovich-Keil
Traditionally, model-based reinforcement learning (MBRL) methods exploit neural networks as flexible function approximators to represent unknown environment dyn…
math.OC2024
On the Stability of Nonlinear Receding Horizon Control: A Geometric Perspective
Tyler Westenbroek, Max Simchowitz, Michael I. Jordan +1
%!TEX root = LCSS_main_max.tex The widespread adoption of nonlinear Receding Horizon Control (RHC) strategies by industry has led to more than 30 years of intense research efforts…