56 citations · 266 across the 35 of their papers we have counts for
4 papers · 1 filter
Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning
Nathan O. Lambert, Albert Wilcox, Howard Zhang +2
Accurately predicting the dynamics of robotic systems is crucial for model-based control and reinforcement learning. The most common way to estimate dynamics is by fitting a one-st…
Nonholonomic Yaw Control of an Underactuated Flying Robot with Model-based Reinforcement Learning
Nathan Lambert, Craig Schindler, Daniel Drew +1
Nonholonomic control is a candidate to control nonlinear systems with path-dependant states. We investigate an underactuated flying micro-aerial-vehicle, the ionocraft, that requir…
Learning for Microrobot Exploration: Model-based Locomotion, Sparse-robust Navigation, and Low-power Deep Classification
Nathan O. Lambert, Farhan Toddywala, Brian Liao +3
Building intelligent autonomous systems at any scale is challenging. The sensing and computation constraints of a microrobot platform make the problems harder. We present improveme…
Objective Mismatch in Model-based Reinforcement Learning
Nathan Lambert, Brandon Amos, Omry Yadan +1
Model-based reinforcement learning (MBRL) has been shown to be a powerful framework for data-efficiently learning control of continuous tasks. Recent work in MBRL has mostly focuse…