6 citations · 14 across the 6 of their papers we have counts for
10 papers
Investigating Compounding Prediction Errors in Learned Dynamics Models
Nathan Lambert, Kristofer Pister, Roberto Calandra
Accurately predicting the consequences of agents' actions is a key prerequisite for planning in robotic control. Model-based reinforcement learning (MBRL) is one paradigm which rel…
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…
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…
Data-efficient Learning of Morphology and Controller for a Microrobot
Thomas Liao, Grant Wang, Brian Yang +4
Robot design is often a slow and difficult process requiring the iterative construction and testing of prototypes, with the goal of sequentially optimizing the design. For most rob…