4 citations · 5 across the 3 of their papers we have counts for
3 papers
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…
Learning Generalizable Locomotion Skills with Hierarchical Reinforcement Learning
Tianyu Li, Nathan Lambert, Roberto Calandra +2
Learning to locomote to arbitrary goals on hardware remains a challenging problem for reinforcement learning. In this paper, we present a hierarchical learning framework that impro…
Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learning
Nathan O. Lambert, Daniel S. Drew, Joseph Yaconelli +3
Designing effective low-level robot controllers often entail platform-specific implementations that require manual heuristic parameter tuning, significant system knowledge, or long…