Alexander Spitzer — accepted AI-conference papers

2024

Demonstrating HOUND: A Low-cost Research Platform for High-speed Off-road Underactuated Nonholonomic Driving

Sidharth Talia, Matthew Schmittle, Alexander Lambert, Alexander Spitzer, Christoforos Mavrogiannis, Siddhartha Srinivasa

RSS 2024poster

Off-road autonomy, crucial for applications such as search-and-rescue, agriculture, and planetary exploration, poses unique problems due to challenging terrains, as well as due to the risk involved in testing or deploying such systems. Accessible platforms have the potential to widen the field to a…

Cited by 5 Source ↗PDF ↗Scholar ↗

2024

Model Predictive Control for Aggressive Driving Over Uneven Terrain

Tyler Han, Alex Liu, Anqi Li, Alexander Spitzer, Guanya Shi, Byron Boots

RSS 2024poster

Terrain traversability in unstructured off-road autonomy has traditionally relied on semantic classification, resource-intensive dynamics models, or purely geometry-based methods to predict vehicle-terrain interactions. While inconsequential at low speeds, uneven terrain subjects our full-scale syst…

Cited by 21 Source ↗PDF ↗Scholar ↗

2023

DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control

Kevin Huang, Rwik Rana, Alexander Spitzer, Guanya Shi, Byron Boots

CoRL 2023oral

Precise arbitrary trajectory tracking for quadrotors is challenging due to unknown nonlinear dynamics, trajectory infeasibility, and actuation limits. To tackle these challenges, we present DATT, a learning-based approach that can precisely track arbitrary, potentially infeasible trajectories in the…

Cited by 27 Source ↗code ↗Scholar ↗

2021

Feedback Linearization for Quadrotors with a Learned Acceleration Error Model

Alexander Spitzer, Nathan Michael

ICRA 2021poster

This paper enhances the feedback linearization controller for multirotors with a learned acceleration error model and a thrust input delay mitigation model. Feedback linearization controllers are theoretically appealing but their performance suffers on real systems, where the true system does not ma…

Cited by 14 Source ↗Scholar ↗

2019

Inverting Learned Dynamics Models for Aggressive Multirotor Control

Alexander Spitzer, Nathan Michael

RSS 2019poster

We present a control strategy that applies inverse dynamics to a learned acceleration error model for accurate multirotor control input generation. This allows us to retain accurate trajectory and control input generation despite the presence of exogenous disturbances and modeling errors. Although a…

Cited by 8 Source ↗PDF ↗Scholar ↗

2017

Experience-driven Predictive Control with Robust Constraint Satisfaction under Time-Varying State Uncertainty

Vishnu Desaraju, Alexander Spitzer, Nathan Michael

RSS 2017poster

We present an extension to Experience-driven Predictive Control (EPC) that leverages a Gaussian belief propagation strategy to compute an uncertainty set bounding the evolution of the system state in the presence of time-varying state uncertainty. This uncertainty set is used to tighten the constrai…

Cited by 23 Source ↗PDF ↗Scholar ↗