Matthew Schmittle — 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 ↗

2023

TerrainNet: Visual Modeling of Complex Terrain for High-speed, Off-road Navigation

Xiangyun Meng, Nathan Hatch, Alexander Lambert, Anqi Li, Nolan Wagener, Matthew Schmittle +8

RSS 2023poster

Effective use of camera-based vision systems is essential for robust performance in autonomous off-road driving, particularly in the high-speed regime. Despite success in structured, on-road settings, current end-to-end approaches for scene prediction have yet to be successfully adapted for complex…

Cited by 62 Source ↗PDF ↗Scholar ↗

2020

Learning from Interventions: Human-robot interaction as both explicit and implicit feedback

Jonathan Spencer, Sanjiban Choudhury, Matt Barnes, Matthew Schmittle, Mung Chiang, Peter Ramadge +1

RSS 2020poster

Scalable robot learning from seamless human-robot interaction is critical if robots are to solve a multitude of tasks in the real world. Current approaches to imitation learning suffer from one of two drawbacks. On the one hand, they rely solely on off-policy human demonstration, which in some cases…

Cited by 66 Source ↗PDF ↗Scholar ↗