Scott Drew Pendleton — accepted AI-conference papers

2022

HiddenGems: Efficient safety boundary detection with active learning

Aleksandar Petrov, Carter Fang, Khang Minh Pham, You Hong Eng, James Guo Ming Fu, Scott Drew Pendleton

IROS 2022poster

Evaluating safety performance in a resource-efficient way is crucial for the development of autonomous systems. Simulation of parameterized scenarios is a popular testing strategy but parameter sweeps can be prohibitively expensive. To address this, we propose HiddenGems: a sample-efficient method f…

Cited by 2 Source ↗Scholar ↗

2017

Numerical Approach to Reachability-Guided Sampling-Based Motion Planning Under Differential Constraints

Scott Drew Pendleton, Wei Liu, Hans Andersen, You Hong Eng, Emilio Frazzoli, Daniela Rus +1

RA-L 2017

This paper presents a new method for motion planning under differential constraints by incorporating a numerically solved discretized representation of reachable state space for faster state sampling and nearest neighbor searching. The reachable state space is solved for offline and stored into a “r

Cited by 19 Source ↗Scholar ↗