activity
20192023
most citedOn the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning

33 citations · 78 across the 12 of their papers we have counts for

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Showing cs.ROShow all

5 papers · 1 filter

cs.RO2021

BotNet: A Simulator for Studying the Effects of Accurate Communication Models on Multi-agent and Swarm Control

Mark Selden, Jason Zhou, Felipe Campos +3

Decentralized control in multi-robot systems is dependent on accurate and reliable communication between agents. Important communication factors, such as latency and packet deliver…

cs.RO2020

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…

cs.RO2020

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…

cs.RO20194 cited

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…

cs.RO20191 cited

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…