Evan C. Carter — accepted AI-conference papers

2026

Exploiting LiDAR Symmetries to Enhance Value Estimation Policies

Yazied A. Hasan, Adrian B. Faust, Evan C. Carter, Lydia Tapia

RA-L 2026

Motion planning problems frequently exhibit geometric symmetry in both action and dynamics. This symmetric property can be exploited in learning-based solutions such as reinforcement learning during inference time, which allows the learned solution to improve without the need for additional training

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2023

Enhancing Value Estimation Policies by Post-Hoc Symmetry Exploitation in Motion Planning Tasks

Yazied Hasan, Ariana M. Villegas-Suarez, Evan C. Carter, Aleksandra Faust, Lydia Tapia

IROS 2023poster

Motion planning tasks are often innately invariant to certain geometric transformations, or in other words, symmetric. This property, however, is not always reflected in learned policies that are trained on these tasks. Although this asymmetry can be addressed through data augmentation or additional…

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