Chris Heckman — accepted AI-conference papers

2023

Tell Me Where to Go: A Composable Framework for Context-Aware Embodied Robot Navigation

Harel Biggie, Ajay Narasimha Mopidevi, Dusty Woods, Chris Heckman

CoRL 2023poster

Humans have the remarkable ability to navigate through unfamiliar environments by solely relying on our prior knowledge and descriptions of the environment. For robots to perform the same type of navigation, they need to be able to associate natural language descriptions with their associated physic…

Cited by 14 Source ↗Scholar ↗

2022

Verified Path Following Using Neural Control Lyapunov Functions

Alec Reed, Guillaume O Berger, Sriram Sankaranarayanan, Chris Heckman

CoRL 2022poster

We present a framework that uses control Lyapunov functions (CLFs) to implement provably stable path-following controllers for autonomous mobile platforms. Our approach is based on learning a guaranteed CLF for path following by using recent approaches --- combining machine learning with automated t…

Cited by 4 Source ↗Scholar ↗